#76 Why You Should Outsource an External Review for Your Data Science with Michael Brand– Founder

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Michael Brand has over 25 years of cutting-edge, international industry experience in advanced analytics, machine learning, artificial intelligence, machine vision, and natural language processing, Dr. Brand’s data expertise is both uniquely wide and uniquely deep. He served as Chief Data Scientist at Telstra Corporation, as Senior Principal Data Scientist at Pivotal, as Chief Scientist at Verint Systems, as CTO Group Algorithm Leader at PrimeSense Ltd (in the machine-vision team that developed the Xbox Kinect), and as Director of the Monash Centre for Data Science in his role as Associate Professor of Data Science at Monash University. He has developed solutions at every scale from on-chip to Big Data, from real-time to high-powered computing, and made industry-defining contributions that have earned him 16 patents (more pending), garnered many prestigious industry and academic awards, and underline $100Ms/pa revenues and $100Ms in valuation for the companies he worked with.

In this episode, Michael explains how he started in the data space by doing a little bit of programming. At the start of his career, he was one of the first people to look at continuous blood pressure measurements. When you go to the doctor, and they measure your blood pressure, it’s a number. Although, nobody knows how to interpret this number. It was assumed this number would be reasonably consistent throughout the day; however, everything that we do influences our blood pressure. For instance, Michael found that when a person wakes up, their blood pressure spikes so high that if a doctor saw that number, they would assume the patient had a heart attack. Then, Michael found himself serving in the Israeli army in a unit dedicated to data science for a full seven years. At the time, they had to invent a lot of the theory, data management, and data science. 

Later, Michael explains his work at Verint Systems. Like all other vendors, the worst their testing set is, the better their results are going to be. They do not need to go back and reconsider if the numbers are right or wrong, there is no incentive. Data science needs to be heading toward a world where it is done in a regulated and transparent way. There is nobody that can see the hump in their own back, no one is perfect. It is challenging to convince companies that they need an external review. There is a mental shift that needs to happen, data science is not a form of magic. Organizations spend millions of dollars on data science, and it increases their risk based on research. Michael knows many large teams that have been closed down because companies are not seeing the value. The reason why it is not happening more is that they do not even realize their data science teams are losing them money. Stay tuned to hear Michael discuss data governance, data rights, and the services Michael’s company offers. 

Enjoy the show!

We speak about:

  • [01:45] How Michael started in the data space

  • [05:35] Capturing brand new blood pressure data 

  • [09:15] What you buy and eat depends on the weather 

  • [15:10] Working with data science in the Israeli army  

  • [19:40] Engineering approach vs. the scientific method approach 

  • [28:45] When is the deep learning madness going to end?

  • [31:00] Working at Verint Systems 

  • [36:05] The core of what Michael currently does

  • [43:20] Tools to ensure secrecy  

  • [47:20] Making strategic decisions with data science

  • [50:00] Every company needs a data strategy   

  • [54:15] Where does data governance play a role in an organization? 

  • [63:10] The need to start talking about data rights

  • [69:20] Listener questions  

  • [76:00] Michael has imposter syndrome 

Resources:

Michael’s LinkedIn: https://www.linkedin.com/in/michael-brand-b230736/
Otzma’s LinkedIn: https://www.linkedin.com/company/otzma-analytics/about/

Otzma Analytics: https://otzmaanalytics.com

Quotes:

  • “When you have data that nobody has ever looked at before, you will see stuff that nobody has ever seen before.”

  • “We are in a world where we are pushed towards thinking of data science as a form of engineering.”

  • “You can outsource a lot of things, but you should do your own testing.”

  • “Every data you encounter is different; the value is understanding how that data is different.”

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Michael Brand is based in Melbourne, Australia.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!


#75 Why We Need More Women Working in Data Science with Amy Daali, Ph.D. – Founder & CEO

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Amy is an Engineer and a multidisciplinary Data Scientist at USAA. She received her B.E degree in Electrical and Computer Engineering at the University of Minnesota and her M.S. and Ph.D. degrees in Electrical Engineering from the University of Texas. Amy is an active promoter for women in STEM and enjoys teaching & organizing community events to increase women's visibility in the field. She previously served as the Women in Data Science Ambassador (WiDS) for the Global Women in Data Science group at Stanford University. She also founded San Antonio Data Science Meetup in 2016. 

Amy began her career at Southwest Research Institute as an Engineer, where she implemented various frequency estimation and tracking methods using Fast Fourier Transform techniques. After completing her Ph.D., she worked as a Postdoctoral fellow at the UT Health Science Center where she developed algorithms to extract insights and evidence into the effectiveness of a new drug using Advanced Image Processing Techniques. 

In this episode, Amy explains how she has always been curious about infinite intelligence. Currently, Amy is the CEO and Founder of Lucea AI - a Healthcare Analytics Consultancy that is passionate about solving challenging problems to benefit society. They accelerate your competitive edge, one algorithm at a time. The healthcare space is lacking artificial intelligence; Amy wants to offer all the help that she can. Amy believes in building communities; you cannot create good work by yourself. 

Then, Amy explains how her Ph.D. changed her entire future; she was not prepared for the amount of work it was going to be. It changed the way she finds problems, solutions, and accepts criticism. To graduate with a Ph.D., you need to create something no one has thought of before. Amy always says that if you can get a Ph.D., you can do anything. Her dad encouraged her to finish her degree; he would always remind her why she started it in the first place. Amy explains how her passion has kept her motivated.

Two years after getting her Ph.D., Amy realized she missed the collaboration and pace of the industry. Academia was slow-paced and included a lot of individualized work. As a consultant, you can learn so much more and at a faster rate. Another thing that is important to Amy is impacting society. She doesn’t want her skills to be used for entertainment purposes. After having kids, Amy became interested in the healthcare world. Being a mom made her a more compassionate person. We can use technology to better the diagnosis of patients; Amy’s first dataset she worked on was detecting breast cancer in women. 

Lastly, Amy explains what it’s like being a female in the data space. She doesn’t think she has been discriminated against; if something weren’t right, she would confront it. However, Amy does realize it is a lonely journey as a woman. She may only see one or two other females in the workplace. Women are scared of the word AI because when they attend data events, it is always men. Amy encourages women to join the world of AI – there are so many opportunities for them in this space. This is why Amy started the San Antonio Women in Machine Learning & Data Science Meetup. Stay tuned to hear how Amy keeps a work/life balance, what she’s most proud of in her career, and advice for aspiring data scientists. 

Enjoy the show!


We speak about:

  • [02:55] How Amy started in the world of data

  • [05:40] Amy’s professional background   

  • [07:40] Amy’s Ph.D. changed everything 

  • [13:00] Deciding to become an entrepreneur  

  • [17:45] Having kids inspired Amy to work in healthcare 

  • [25:20] Amy’s passion has kept her motivated 

  • [27:50] Being a female in the data space 

  • [35:40] About the Women in Data Science Meetup 

  • [42:00] Having a work/life balance 

  • [44:25] What Amy is most proud of in her career 

  • [49:30] Amy’s advice for aspiring data scientists 

Resources:

Amy’s LinkedIn: https://www.linkedin.com/in/amywdaali/
Amy’s Twitter: https://twitter.com/wdaali999?lang=en

Lucea AI LinkedIn: https://www.linkedin.com/company/lucea-ai/about/

Lucea AI Website: https://www.lucea-ai.com

Women in Data Science: https://www.widsconference.org/ambassadors-2018.html

San Antonio Women in Machine Learning & Data Science: https://www.meetup.com/San-Antonio-Women-in-Machine-Learning-and-Data-Science/events/

IEEE Smart City Summit: https://attend.ieee.org/scs-2019/speakers/

Quotes:

  • “Data has the ability to help a lot of people.”

  • “If you can do a Ph.D., you can do anything.”

  • “I love the fast pace of the industry.”

  • “You don’t need a PhD to be a data scientist.”


Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Amy Daali is based in San Antonio, Texas, USA.


And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!



#74 Creating Value using Artificial Intelligence with Ru Mitra– Founder and Author

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Ru is a graduate from the University of Cambridge, UK who has built six startups in four countries. His primary interest is to build products with social Value. He is also a mentor of Google Launchpad and a senior AI advisor of EFMA Banking group. Ru has been invited to speak at over 60 events from 21 countries. His talks are about sharing his experiences on growing various startups and building products in Artificial Intelligence and Machine Learning.

In this episode, Ru tells us that he started his career in AI because he loves mathematics and solving problems. He has been making bleeding-edge applications since the early 2000s then got into startup land. Ru was always surprised by how difficult it was selling technology. He thought what he was building was more advanced and better than anything else out there. Just because you make great technology, does not mean you can sell it to a business. Some of his startups failed, so Ru has learned a lot from these many challenges. 

Ru realized that young people paid higher insurance premiums just because they do not have a more extended driving history. So why don’t they track their driving and have the insurance company calculate their premium? When they asked the insurance companies if they would like to be their customers, naturally they said no. This was a challenge that the startup faced. Their users were young drivers, so they tried to create a community of drivers who were interested in what they were building and wanted to be part of it. They had millions of drivers and collected their data. Now, the insurance companies were interested because they had the data. 

Then, Ru talks about his writing career. People from around the world started inviting him to speak at events after they read his work. He’s not a professional public speaker, nor does he want to be. The idea of doing many things is a way to find the true potential that we truly have. Our potential is way more significant than we think, the only way we will know is by trying new things. If you are comfortable, then you are not growing. Now, Ru found what he wants to do for the rest of his life. He has always been interested in the education sector. Ru believes we should contribute to this world and to those who may not be as lucky. Stay tuned to hear Ru discuss his advisory roles, meditation, and overcoming challenges with data.  

Enjoy the show!

We speak about:

  • [01:45] How Ru started in the AI space

  • [06:50] What surprised Ru about startups 

  • [10:15] Getting data first, then finding customers 

  • [15:00] Ru’s writing career  

  • [18:35] How to live a complete life

  • [20:40] What success means to Ru 

  • [31:10] Bringing to life the machine learning models   

  • [35:00] Ru’s advisory roles 

  • [39:00] OpenAI

  • [41:20] Overcoming challenges with data 

  • [44:00] Challenges with user adoption 

  • [50:00] What Ru is most proud of

  • [51:40] Advice for the audience 

Resources:

Ru’s LinkedIn: https://www.linkedin.com/in/mitrar

Ru’s Website: https://www.mitrarudradeb.com

Creating Value With Artificial Intelligence: Lessons Learned from 10 yrs of Building AI Products and Overcoming Data, Adoption, and Engineering Challenges

Quotes:

  • “I overcome challenges by learning from my failures.”

  • “Two years ago I would have never thought I would be a good public speaker.”

  • “Success cannot be defined by external factors.”

  • “I haven’t been stressed for three years, maybe more.”


Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Rudradeb Mitra is based in Stockholm, Sweden.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

#73 Powering Change using AI with Alex Ermolaev – AI Leader

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Alex Ermolaev has been involved in the software industry for 20 years, including AI-specific experience at Bell Labs, Microsoft, several startups and now Nvidia. He is currently a leading AI software developer and works with groundbreaking companies that are implementing incredible AI solutions across several domains. 

In this episode, Alex describes how he started in the data space. Early in his career, he got a chance to work on a lot of data and software products. His first application that he enjoyed was expert systems. Expert systems are an interesting technology; however, it worked quite well in very few scenarios. Sometimes people spent a lot of time and budget on these systems with no return. Please don’t assume that technology will be able to do everything you need it to do before spending time and money. Alex came to the United States for an internship at Bell Labs, then worked for Microsoft, and startups where he did several AI projects. Some people try to use AI to ensure a TV advertisement is shown at the appropriate time or not, whereas some people will use it for brand logo stuff. About five years ago, AI started getting much attention again. It is possible to achieve higher accuracy with AI systems than anyone was able to see before. Because of this, Alex decided to dedicate all of his time to AI projects and nothing else.  

Then, Alex and Felipe discuss business development skills. It is easy to sit in an office and assume the world works in a certain way. The only way to understand how the world truly works is by getting out and experiencing it. He went for a business trip and saw so many new things, got new information, and made new contacts. When he comes back to the office, nothing has changed. You realize the company will operate in a certain way despite all the changing aspects outside of those walls. Sometimes the company will see business development as an external process. When Alex was looking for a company who was taking advantage of the AI resurgence, he came across Nvidia. Thousands of people are working with AI for Nvidia now. Alex helps organizations across the board adopt the tools of AI for their particular applications. He loved working and interacting with people trying to solve problems. 

Then, Alex speaks about the successes and failures he saw when working for companies and their AI technology. Alex says AI is finding patterns in data to predict or detect something. There are many cases where patterns do not exist. For instance, in stock markets, patterns tend to be very weak because changes in the stock market tend to be very external. People try to apply AI to the stock market, but the pattern is very weak. Computer viruses and fraud are examples of successful AI patterns. Fraud tends to happen in patterns, whether it is telecom fraud or e-commerce fraud. If you detect a pattern, you can apply it to future cases to stop fraudulent activities. Stay tuned to hear Alex discuss how to tackle data problems using AI, some exciting uses for AI, and what Alex is most proud of in his career. 

Enjoy the show!

We speak about:

  • [01:50] How Alex started in the data space

  • [04:55] Alex’s professional background  

  • [10:30] Working for the finance team at Microsoft 

  • [14:55] Business development skills  

  • [18:50] Challenges working with startups 

  • [22:10] Working at Nvidia  

  • [26:20] Successful and unsuccessful AI patterns  

  • [30:00] AI and collecting data 

  • [35:15] How to tackle data problems using AI 

  • [40:30] Exciting uses for AI  

  • [43:15] The execution of new AI programs  

  • [49:00] What Alex is most proud of  

  • [50:20] Be patient and invest in your knowledge   

Resources:

Alex’s LinkedIn: https://www.linkedin.com/in/alexermolaev


Quotes:

  • “The best way to develop knowledge in any area is to experience it.”

  • “It is easier to sit in an office and assume the world works in a certain way.”

  • “Don’t be in startups because it’s cool, try and find a path that meets your own needs.”

  • “Working with startups is a lot of broader outreach and helping the community understand what is possible.”

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Alex Ermolaev is based in San Francisco, California.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

#72 Focusing on Simplification to Solve Data Science Roadblocks with Evan Shellshear – Head of Analytics

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Evan Shellshear has been an entrepreneur for more than a decade, and throughout that time he has always loved getting his hands dirty with building products from scratch and then commercializing them. Evan has a passion for innovation and not just from a managerial perspective but also from a doing perspective. He has a Ph.D. in Game Theory, is published in fields computer graphics to politics, mathematics to manufacturing, and much more. Evan has founded or co-founded over half a dozen companies to commercialize different technologies. 

In this episode, Evan explained how he started in the data space, including his Ph.D. in Game Theory and hitting rock bottom in Germany. Then, Evan describes what to do if you are stuck in the details and you have hit a roadblock, and you cannot go any further. Imagine running around on a piece of paper, and someone draws a line in front of you, the best solution you can do is to zoom out and see how you are in a three-dimensional world. The best way to solve technical roadblocks is by zooming out and asking what the problem is you are trying to solve. If you still cannot solve the barrier, zoom out another level, and ask what the client is trying to achieve. Instead of solving by being technically smarter, you can solve it by understanding the problem better. 

In Sweden, Evan worked with robotics and took a different approach than the rest of the world to solving deep fundamental issues in robotics around path planning. Instead of using conventional techniques, they chose to open up and find all new avenues of exploration. One thing data scientists are taught is to be highly technical; they are not taught how to learn things. Whenever Evan lectures, he tells people, however complicated their first solution is, look at it and simplify it, then simplify it again. It needs to be simplified twice before then can present it to anybody. Then, Evan explains the measures of success he uses during a project to ensure he is on the right track. When you notice your client has not even considered the problem you are presenting is what Evan calls an “aha moment.” Later, Evan explains the process of a case study, getting users to adopt new technologies and his book Innovation Tools.

Enjoy the show!

We speak about:

  • [01:15] How Evan started in the world of data

  • [09:45] Zoom out to solve technical roadblocks 

  • [12:10] Examples of how Evan zoomed out

  • [14:25] Why is zooming out a challenge for data scientists?   

  • [18:00] Focus on simplification

  • [22:45] Taking opportunities that present themselves   

  • [27:00] Measures of success during a project 

  • [31:00] The process of a case study 

  • [36:05] Getting users to adopt new technologies  

  • [40:00] Innovation Tools

  • [47:20] Evan’s proudest moment 

  • [49:40] Challenges for the future of machine learning  

  • [51:30] Get soft skills   

Resources:

Evan’s LinkedIn: https://www.linkedin.com/in/eshellshear/

Innovation Tools: https://amzn.to/2OrrAsj

Quotes:

  • “Take a step up and over to look at the problem in a new direction.”

  • “It is in our human nature to overcomplicate things.”

  • “I need to help the company understand what the true problem is.”

  • “Take a low-risk approach to solve your client’s problem.”


Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Evan Shellshear is based in Kensington, Victoria, Australia.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Creating Effective Data Science Presentations with Rachel Fojtik – Director of Analytics and Performance

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Rachel Fojtik is an Experienced Senior leader in Analytics, influencing change in behaviour, company culture, and improvement with analytics. Managing high performing teams that deliver across a myriad of knowledge areas. She is passionate about delivering information that sees results, using collaborative design and development. A demonstrated history of setting up teams that deliver end to end business intelligence implementations. Cross-industry experience in healthcare, telecommunications, the financial services industry, travel and tourism, and energy.

In this episode, Rachel explains how she started in travel and tourism rather than data science. She did a couple of short stints in building databases; this is where she realized her interest in numbers. Her passion for data grew from the excitement when her director realized the profitability of their company just by changing a particular practice. In Rachel’s roles, she got heavily involved in financial performance metrics, employee’s quality of work, and HR. Plus, Rachel explains the difference between working with the management director vs. operational work.  

Then, Rachel discusses how she loves working stuff out and finding her way around something to make it the most efficient. You shouldn’t start with the constraints in front of you, take them off the table, and find the best way to do it. Do not just go for the easiest route, find a better way, and improve on it. Also, Rachel touches on ensuring a presentation is well put together to win over the trust of others. Make sure it is consistent, easy to digest, and professional looking. This will lower the barriers and make the message a lot more accessible. When you are building a dashboard, you are building it for multiple users, so it is challenging to create a story; however, it should still be well presented to highlight what you are getting at. Later, Rachel discusses the role of a product manager, organic governance in the workplace, and her role as Director of Analytics and Performance. 

Enjoy the show!

We speak about:

  • [01:15] How Rachel started in the data space

  • [08:40] The motivation behind Rachel’s trailblazing  

  • [11:30] The metrics Rachel was helping optimize 

  • [14:10] Working with the management director vs. operational work

  • [16:45] Data matching at Diner’s Club  

  • [22:15] Using a minimalist view 

  • [24:45] Find the best way – don’t just stick with what you know  

  • [28:45] If something is well presented, it is more likely to be trusted 

  • [35:00] What is a product manager? 

  • [42:50] An organic governance in the workplace 

  • [46:15] Rachel’s role as Director of Analytics and Performance  

  • [53:00] Building and working on a network  

  • [54:10] Do what you’re passionate about

Resources:

Rachel’s LinkedIn: https://www.linkedin.com/in/rachel-fojtik-78321199/

Quotes:

  • “I created an input tool where a user could design the layout of their input form.”

  • “I’ve always tried to go with a minimalist view.”

  • “If your presentation is way too busy, it is difficult to take a story from that information.”

  • “Consider where the eye goes first when creating a presentation.”

Rachel Fojtik is based in Brisbane, Australia.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!


#70 Making Black Box Models Explainable with Christoph Molnar – Interpretable Machine Learning Researcher

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Christoph Molnar is a data scientist and Ph.D. candidate in interpretable machine learning. He is interested in making the decisions from algorithms more understandable for humans. Christoph is passionate about using statistics and machine learning on data to make humans and machines smarter.

In this episode, Christoph explains how he decided to study statistics at university, which eventually led him to his passion for machine learning and data. Starting out studying with a senior researcher gave Christoph exposure to many different projects. It is an excellent program for students and companies whom both benefit greatly. Christoph learned so much about statistics that he would not have been able to acquire otherwise. The clients got nine hours of consulting for free, which is very valuable for their businesses. When Christoph started his statistical consulting career, he did patient analysis to assess if a medication was affecting the spine. He found this very interesting as it differed significantly from his previous consulting.

When labeling data, Christoph says to label and always compare continuously. For instance, when a student labeled one photo, later on, Christoph would show a student the same photo and see if it got labeled identically. Sometimes people will see the same image but label it differently; so, this is one thing you can do to ensure labeling data is going smoothly. If you have multiple labelers, you will need to compare how each labeler will mark the same photo. Do not be blind to the quality of your data; it is easy to adjust the numbers. 


Then, Christoph speaks about pursuing his Ph.D. in Interpretable Machine Learning. He publishes his book, Interpretable Machine Learning, on his website chapter by chapter. Christoph gets feedback and uses it while continuing his writing on future chapters. Learning about interpretable machine learning is not exactly present at university now. Some schools and professors are starting to integrate it into the curriculum. Stay tuned to hear Christoph discuss accumulated local effects, deep learning, and his book, Interpretable Machine Learning.

Enjoy the show!

We speak about:

  • [02:10] How Christoph started in the data space

  • [09:25] Understanding what a researcher needs

  • [15:15] Skills learned from software engineers 

  • [16:00] Statistical consulting 

  • [19:50] Labeling data  

  • [23:00] Christoph is pursuing his Ph.D.

  • [29:00] Why is interpretable machine learning needed now? 

  • [31:00] Learning interpretability  

  • [33:50] Accumulated local effects (ALE)

  • [37:00] Example-based explanations  

  • [39:15] Deep learning  

  • [43:35] The illustrations in Interpretable Machine Learning.

  • [49:50] How Christoph maximizes the impact of his time

Resources:

Christoph’s LinkedIn: https://www.linkedin.com/in/christoph-molnar-63777189/
Christoph’s Website: https://christophm.github.io

Interpretable Machine Learning: https://christophm.github.io/interpretable-ml-book/

Quotes:

  • “Always look at the process when labeling data.”

  • “After each chapter of my book, I publish it and get feedback.”

  • “I randomly read a lot of papers and structure the knowledge to fit them together.”

  • “I express what I want easier with illustrations in my book.”


Christoph Molnar is based in Munich, Bavaria, Germany.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!


#69 Creating Cultural Transformations Using Data Science Leaders with Bülent Kiziltan – Head of Data Science & Analytics and Chief Data Scientist

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Dr. Bülent Kiziltan is an AI executive and an accomplished scientist who uses artificial intelligence to create value in many business verticals and tackles diverse problems in disciplines ranging from the financial industry, healthcare, astrophysics, operations research, marketing, biology, engineering, hardware design, digital platforms, to art. He has worked at Harvard, NASA, and MIT in close collaboration with pioneers of their respective fields. In the past 15+ years, he has led data-driven efforts in R&D and built multifaceted strategies for the industry. He was a scientist at Harvard and the Head of Deep Learning at Aetna leading and mentoring more than 200 scientists.

In this episode, Bülent opens up the show discussing his background at places like Harvard, MIT, and NASA. Then, he explains his transition from astrophysics to business the industry. He was intimately aware of the basic processes and procedures you use to extract information from complicated data streams.

Data leaders want to drive decisions and have informative interactions with your team members. If you HAVE a hard time understanding the ins and outs of different models, it will create an environment that does not make the technical team very happy. From a retention perspective, it will create a challenge for the company. Retention is a significant cost for the organization, having a meaningful conversation with leaders is one way to improve retention. If you approach data science in a limited fashion, you will go after the low hanging fruit and will miss on building long term strategies. Later, Bülent gives recommendations for implementing a constructive culture in the workplace, explains how a leader chould balance priorities, and the nuances of hierarchy in the startup space.

Enjoy the show!


We speak about:

  • [02:00] Bülent’s background

  • [05:50] The transition from astrophysics to business 

  • [08:45] Data leaders need technical experience 

  • [12:45] Academics still need soft skills 

  • [19:20] What data science can offer organizations 

  • [23:50] Addressing causal inferences

  • [25:30] Recommendations for implementing culture in the workplace 

  • [30:00] How a leader should balance priorities  

  • [36:10] Challenges Bülent currently faces in the industry  

  • [38:15] Hierarchy in the startup space

  • [40:45] What Bülent loves about data science 

  • [42:45] Future data challenges   


Resources:

Bülent’s Website: http://www.kiziltan.org/

Bülent’s LinkedIn: https://www.linkedin.com/in/bulentkiziltan/

Quotes:

  • “Culturally, I was surprised by the mindset of business leaders.”

  • “We asked individual members of the data science group to come up with their own ideas that can be implemented in the day-to-day business operations.”

  • “A diverse team is critically important for the business.”

  • “All companies will become AI companies in one way or another.”


Bülent Kiziltan is based in Cambridge, Massachusetts.


And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!





#68 How To Build Award Winning Data Products with Nick Blewden – Head of Data Product Development

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Nick loves playing with, analyzing and visualising data and gets a massive kick out of the change it can bring to people, businesses, and the world. At Lloyd's of London, Nick leads a team of great designers and developers helping people automate processes and get more insight from their data. He gets a buzz from saving others time and surprising them with what surprising insights lie in their data. Out of the office, Nick is keen to mentor or share his experiences and enjoys speaking at events or conferences. 

In this episode, Nick discusses some of his favorite projects and describes issues he has faced being part of various teams. When overcoming team obstacles, he listens to every person in the group. If you do not listen to people, then you cannot persuade someone that you are a good guy. Transparency is also essential; explain what knowledge you bring and the processes that you do. People can pick up on integrity, but they can also pick up on suspicion.

Currently, Nick works at Lloyd's of London, the world's leading insurance market providing specialist insurance services to businesses in over 200 countries and territories. He has helped establish a vision and strategy for Business Intelligence within Lloyd's of London and external market published insight. He delivers automated online MI apps to a range of business functions through a roadmap of strategic change while creating a training structure to develop BI analysts across the business.

Nick's data product development team has to work with other teams at Lloyd's of London to create the best products for their customers. They communicate with the innovation team to understand the research. They also work with all the different modelling teams to access their expertise and bounce ideas around with each other. Before working at Lloyd's of London, Nick was self-employed; it taught Nick a lot about customer service, collaboration, and teamwork. Later, Nick explains common mistakes in developing data products, winning global hackathons, and what excites him most about the future of data.

Enjoy the show!


We speak about:

  • [01:10] How Nick started in the data space 

  • [07:00] The evolution of data warehousing 

  • [11:45] Nick’s favorite projects  

  • [14:45] Navigating team issues 

  • [15:30] Solving problems at Lloyd’s of London 

  • [20:00] Lloyd’s of London customers 

  • [25:40] Interaction with other teams

  • [30:30] Working for yourself

  • [35:45] Common mistakes in developing data products  

  • [38:50] Winning global hackathons 

  • [40:20] What excites Nick most about the future of data 

  • [45:45] Nick’s proudest moments 


Resources:

Nick’s LinkedIn: https://www.linkedin.com/in/nicholasblewden


Quotes:

  • “It is hard to balance being a consultant and running a consultancy company.”

  • “If you are self-employed and your customer is unhappy, that is an extreme scenario for you.”

  • “People do not understand what data products are.”


Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

Visit online.rmit.edu.au for more information


Nick Blewden is based in London, United Kingdom.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!



#66 How to Structure a Data Analytics Team with June Dershewitz – Director of Analytics

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June Dershewitz has spent her career driving analytics strategies for major businesses. She's currently Director of Analytics at Twitch, the world's leading video platform and community for gamers (a subsidiary of Amazon). As an analytics practitioner, she builds and leads teams that focus on marketing analytics, product analytics, business intelligence, and data governance. In her prior life as a consultant, she was a member of the leadership team at Semphonic, a prominent analytics consultancy (now part of Ernst & Young). As a long-standing advocate of the analytics community, she was the co-founder of Web Analytics Wednesdays; she's also a Director Emeritus of the Digital Analytics Association and a current Advisory Board Member at Golden Gate University. She holds a BA in Mathematics from Reed College in Portland, Oregon.

 In this episode, June says she fell into data by accident, she has a bachelor's degree in math. She got an idea that she wanted to move to San Francisco and work with a startup. June was invited for an interview as an analyst, and she loved it. It is only a matter of time for June to meet someone before she starts talking about goats. June grew up on a goat farm; her mother was a farmer. After college, June got a job working for a mathematician in Philadelphia. Later, June decided she wanted to work in the industry. She and her boyfriend moved to San Francisco, and both ended up getting jobs with startups. 

June says in startups; people wear many hats. If you come across a problem, you have the liberty to take ownership and solve it. In smaller companies, it is easier to get a holistic view of the workplace. It is possible at more substantial companies too; June currently works for Twitch, which is owned by Amazon. When June arrived, there were 400 employees, but now has over 1500. One skill June has gotten to use to is stepping up to own things. She still feels empowered to solve problems and to take ownership of those problems. Business intelligence means lots of things to lots of people. Before coming to Twitch, June never claimed to own it outright; however, because she is farther along in her career, she has more experience to draw back on. One risk that we have as data people is potentially getting pigeonholed into a thing that we are good at and getting stuck there. June knows a ton about Adobe Analytics; she could have continued to exist in that realm and be just fine. However, she thrives on solving a multitude of problems and being challenged in the workplace. Later, June discussed the hub and spoke organization model, the data quality journey at Twitch, and getting involved in the data science community. 

Enjoy the show!

We speak about:

  • [01:40] How June started in the data space

  • [08:20] Solving problems in startups 

  • [09:45] Getting a holistic view in the workplace  

  • [11:20] Feeling unsure about owning a piece of work 

  • [15:30] Business intelligence skillsets for data scientists   

  • [19:35] Clear understanding of data roles in the workplace 

  • [20:55] An overview of June’s teams’ structures 

  • [27:10] Managing career transitions with the hub and spoke model  

  • [29:25] Assigning each person a technical buddy  

  • [32:10] The data quality journey  

  • [41:40] Evolution of data quality at Twitch  

  • [48:00] Becoming involved in the data science community  

  • [53:10] Other ways June stays involved in her communities 

  • [55:20] Advice for breaking into the data science field 

Resources:

June’s LinkedIn: https://www.linkedin.com/in/jdersh

Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success

Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program

Quotes:

  • “The thing about being a data person at that time was we just had to figure it out.”

  • “I was the vice president of everything that needed to get done.”

  • “At Twitch, we don’t have a clear definition of what a data engineer means.”

  • “We chose to move to an organization model that is hub and spoke.”

  • “Data governance can mean lots of things to lots of people.”

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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June Dershewitz is based in San Francisco, California.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

#65 Using the Love@Work Method to Improve Workplace Culture with Olivia Parr-Rud – Speaker, Award-Winning Author, and Data Scientist

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Olivia is an internationally known thought-leader, speaker, best-selling and award-winning author, and a data scientist who focuses on the interplay between technology, corporate leadership, and personal growth and happiness.  Throughout her career, she has blended analytic tools and holistic organizational practices to deliver successful solutions for her clients. As a lifelong spiritual seeker, Olivia began to see patterns that revealed the importance of love as a driver of business success.

In this episode, Olivia explains why she changed her major to statistics in grad school. Once she completed her degree, she joined a bank in San Francisco. Olivia built a model using logistic regression for the bank. It saved the company 17 million dollars a year in mail expense, making her an instant hero. Her desktop computer had a 500-megabyte hard drive when she was running SAS she couldn’t get into any other programs. Financial services had a vibrant climate for modelling because the behavioral data was so reliable. Behavioral data is so powerful because if a person has done something before, they are more likely to do it again.

Then, Olivia gives tips for explaining data science to non-technical people. Listen to what their goals and concerns are; then, speak and show the results that address their concerns. Data visualizations help because they are bright and easy to absorb. After her first publication, Data Mining Cookbook, Oliva started traveling and speaking at conferences. Her first book was a complete brain dump, whereas her second book, Business Intelligence Success Factors, was a lot of research. It was designed to take someone with a critical-thinking mind, and give them an argument for treating people well. 

Later, Olivia discusses Holacracy and why she believes this is a good model for where businesses will go in the future. Companies have to be much more agile. Holacracy allows knowledge and wisdom to come from everyone. In the future, companies will need to be able to turn on a dime to adapt and stay competitive. Holacracy is an agile idea that allows for companies to change how they operate immediately. Also, using Holacracy, people are allowed to create teams and are not necessarily solely autonomous. Stay tuned to hear Oliva and Felipe discuss the human side of data, the Love@Work Method, and using LEAP to improve workplace culture. 

Enjoy the show!

We speak about:

  • [01:40] How Olivia started in the data space  

  • [07:50] Data in the financial services industry 

  • [08:50] Oliva’s career history  

  • [13:35] Starting a consulting business 

  • [17:10] Tips for explaining data science to non-technical people

  • [18:30] Becoming a published author 

  • [24:45] Learning about Holacracy  

  • [29:00] Balancing Holacracy and teamwork 

  • [31:40] Combing data and human skills 

  • [40:20] The Love@Work Method

  • [47:15] One of Oliva’s professional fails   

  • [51:10] Using LEAP (love, energy, audacity, and proof)

  • [54:30] Following our intuitions 

Resources:

Oliva’s Website: www.lovemakeityourbusiness.com

Data Science Consulting: www.oliviagroup.com

 My Big ‘Why’ - https://tinyurl.com/LOVENEWCOMPETITIVEEDGE

LOVE@WORK now available at https://tinyurl.com/OLIVIAPRLOVEATWORK - A Silver Nautilus Book Award-Winner 

The LOVE@WORK MethodTM now available at https://tinyurl.com/TheLOVE-WORKMethod

What is your Corporate Love Quotient? Find out here www.corporatelovequotient.com   

Love@Work Method to Improve Workplace Culture with Olivia Parr-Rud

Oliva’s Social Media:

Facebook: https://www.facebook.com/LoveMakeItYourBusiness/

LinkedIn: https://www.linkedin.com/in/oliviagroup/

Twitter handle: #OliviaParrRud  

YouTube: www.OliviaOnYouTube.com

Instagram: Love.MakeItYourBusiness

Quotes:

  • “Companies need to be able to adapt much more quickly now.”

  • “As more things get automated, the need for human factors are much more prominent.”

  • “Each manager should speak to their direct reports at least once a day.”

  • “People who have better experiences at work go home happier.”


Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Olivia Parr-Rud is based in Los Angeles, California.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

#64 Intersections of Analytics, AI, Linguistics and Culture with Prashant Natarajan – Principal, AI & Analytics

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Prashant Natarajan has 18+ years’ experience in building EMRs, ERP, big data platforms, actionable analytics, and machine/deep learning applications. Before joining Deloitte, he served in hands-on global consulting and product leadership roles at H2O.ai, Oracle, McKesson Payer Solutions, Healthways, and Siemens. Prashant is Co-Faculty Instructor of Data Science and AI at Stanford University School of Medicine, Palo Alto, CA, USA. He volunteers as an industry expert and guest lecturer at leading Australian universities. Prashant serves as an industry advisor at the CIAPM computer vision project in University of California San Francisco, Council for Affordable Health Coverage, and Pistoia Alliance Center for Excellence in Artificial Intelligence.  

In this episode, Prashant describes how essential human interaction is for success. In a technology-heavy space, human interaction and linguistics were not very common. Instead of complaining about it, Prashant went and got his masters to focus on English in the technology space. To have success, we need a clear understanding of culture. Culture is language, and language at its core is mathematics. How do we interact with people to figure out what their strengths are? Prashant considers himself the luckiest person on earth to have the experiences he has had in his career. 

Then, Prashant discusses how to identify business problems and integrate it with data science. Data analytics is as old as when humans started interacting with each other. By nature, all human beings are data analytics, consuming creatures. Historically, the application of computing data and analysis have been humans trying to define a problem, find data to solve the problem, and then writing algorithms that will create insights. Prashant is a massive admirer of the human mind and the human brain, which is far superior to any artificial machine. When it comes to AI, computing can do things that the human mind cannot. Business leaders must look at data science as a way to help them define business problems, rather than purely using deduction. Later, Prashant advises companies moving into data-driven products, explains horizontal capabilities, and the use of machine learning in healthcare. 

Enjoy the show!

We speak about:

  • [01:25] How Prashant started in the data space 

  • [03:45] Studying communications and linguistics  

  • [08:45] Mentoring young professionals  

  • [11:45] Work with people who are smarter than you  

  • [15:00] Merging business problems with data science  

  • [19:45] The value business leaders see in data  

  • [25:00] Advice for companies who are moving into data-driven products

  • [29:45] What excites Prashant about the future of data 

  • [34:05] Horizontal capabilities 

  • [37:20] The use of machine learning in healthcare  

  • [44:20] Improving product development 

  • [48:40] Prashant’s proudest moment

  • [50:15] The manufacturing industry 

  • [52:20] We learn more from our failures than our successes 

Resources:

Prashant’s LinkedIn: https://www.linkedin.com/in/natarpr/

Demystifying Big Data and Machine Learning for Healthcare (Himss Book)

Quotes:

  • “Human interaction is the most key determiner of success or not.”

  • “Today, we have the technology that has caught up with the human need.”

  • “Data science is increasingly a horizontal capability that will impact all of us.”

  • “I celebrate relationships because they allow me to learn.”

Now you can support Data Futurology on Patreon!

https://www.patreon.com/datafuturology

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Prashant Natarajan is based in Melbourne, Victoria, Australia.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

#62 Full Stack Data Science with Gregory Hill – Global Head of Analytics

Gregory Hill.jpg

Dr. Gregory Hill leads the Analytics function at Brightstar's Global Services division, developing and delivering their data & analytics strategy, innovation programs, and product development initiatives. He works across their lines of business, including supply chain optimization, product portfolio management, financial services, buy-back and trade-in, leasing, and omnichannel solutions. He also manages Brightstar's analytics team in support of their key global accounts with pre-sales, solution design, and service delivery. His expertise is in the application of advanced analytics techniques (including machine learning, predictive modelling, mathematical optimization, econometrics, and operations research) to commercial problems. These applications span forecasting, pricing, fraud, market segmentation, customer satisfaction, and propensity modelling.

In this episode, Gregory explains how he started in the data space. He was aware of all the theoretical work being done around data but did not know how it worked in an industry aspect. The real challenge of putting mathematical models to practice lies in the organizational and people elements of it. Computer science and electrical engineering do not teach you how to overcome organizational challenges and individual motivations and incentives. Going back to get his Ph.D., Greg wanted to do something requiring qualitative research. So he targeted informational systems and economics. His fieldwork leads him to interview executives of larger banks, publicly listed companies, and government agencies. He came up with an economic framework that improved customer data quality. 

Some problems Greg started looking into while working at Telstra were fixed by using the four P's of marketing. He had an opportunity to learn something new that they did not teach in engineering school. In business, the four P’s are a useful lens to think about commercial problems like product lifecycle management and portfolio optimization for business. They looked at questions around what products will work well in what channels. Previously, this type of merchandising decision making was done by gut feel. Having a data-driven approach was a different way of thinking for the company and the teams. Greg would not recommend someone to gain a Ph.D. to become a data scientist. You can acquire the skills you need outside of academia. Academia will not give you the skills to become successful, a Ph.D. may hinder requiring all the skills to become a data scientist. Later, Greg discusses his appreciation for managing data scientists, being involved in the local data community, and the challenges of working globally. 

Enjoy the show!

We speak about:

  • [02:00] How Greg started in the data space

  • [11:10] Leaving academics and getting involved in the industry  

  • [13:20] Greg’s work background

  • [18:25] The four P’s of marketing

  • [20:40] Transiting from gut instinct to a data-driven approach

  • [27:55] Thinking through cause and effect 

  • [30:45] What Greg’s team looks like

  • [39:00] Lessons learned from managing data scientists  

  • [42:25] Active in local data science meetups + guest speaking  

  • [44:25] Working globally + peeling back opportunities to use data science techniques

Resources:

Greg’s LinkedIn: https://www.linkedin.com/in/gregoryhill/?originalSubdomain=au

Brightstar: https://www.brightstar.com

Quotes:

  • “My thesis was not a project; it was a lifestyle.”

  • “I didn’t want to be an academic, I wanted to get back into the industry.”

  • “It was a combination of arrogance and laziness.”

  • “At the end of the day, it boils down to if I change X, will Y change?”

Now you can support Data Futurology on Patreon!

https://www.patreon.com/datafuturology

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Gregory Hill is based in Melbourne, Australia.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

#61 Data Science Strategy in the Military, Startups and Tech/Digital Leaders with Sveta Freidman - Data Analytics & Science Director/Mentor

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Sveta Freidman is a data scientist and business intelligence leader with extensive experience in consulting and client-based environments. She has a vast experience working in different industries, including gambling, retail, health, and online businesses (startups). Sveta is a data strategist with a passion for connecting people to the data they need to make decisions, build better products, and execute marketing strategies.

In this episode, Sveta explains why she decided to study statistics, she had a passion for mathematics. During her time in Israel’s military, she collected data from different places and made sense from it. Her commercial experience comes from various startups she joined. When joining a startup, you have to wear many hats. Sometimes you have to be a data engineer, data scientist, or a data analyst. Then, Sveta moved to Australia and found a startup, Envato, where she built all the data from scratch.


Enjoy the show!

We speak about:

  • [01:40] How Sveta started in the data space

  • [07:15] Sveta’s professional background

  • [17:10] Investing in local talent

  • [20:45] How to hire for a startup

  • [24:30] Questions for hiring interviews

  • [29:25] Working for Carsales

  • [31:55] People not trusting the data

  • [35:20] Solving the issue of trust

  • [40:30] Finding bias in the data

  • [44:50] Make sure you look at the data every day

Resources:

Sveta’s LinkedIn: https://www.linkedin.com/in/sveta-freidman-5981593

Carsales: https://www.carsales.com.au


Quotes:

  • “Statistics is everywhere.”

  • “You can be a great data scientist, but you need to understand the culture.”

  • “I give the candidate a business problem to see how they will react to it.”

  • “Your algorithms are good as long as your data is good.”

Now you can support Data Futurology on Patreon!

https://www.patreon.com/datafuturology

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

Fyrebox - Make Your Own Quiz!

Sveta Freidman is based in Melbourne, Victoria, Australia.


And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

#60 Building Self-Driving Cars in Silicon Valley with Vladimir Iglovikov, Ph.D. – Senior Computer Vision Engineer and Kaggle Grandmaster

vlad.jpeg

Vladimir Iglovikov graduated from university with a degree in theoretical physics, he moved to Silicon Valley in search of a data science role in the industry. This led him to his current position in Lyft’s autonomous vehicle division where he works on computer vision related applications. In the past few years, he has invested a lot of time in Machine Learning competitions leading to his title of Kaggle Grandmaster.

In this episode, Vladimir explains how difficult it was to find work in Silicon Valley. He had harsh requirements for a salary, no one looked at his resume. Companies in Silicon Valley are willing to pay big bucks, but at the same time, they require the person to be skilled in software engineering, machine learning, and statistics. His biggest issue when applying for jobs was assuming that all people are similar to the people in academics. At his interviews, he felt no connection with the interviewers. After sending his resume to over 200 different companies, someone finally bit just before his visa expired. Vladimir worked at Bidgely for 8 months then moved to TrueAccord and eventually got his job at Lyft. 

As a manager, you need to learn to communicate and excite people in different teams about your project. When people come from academia, they are smart on paper but may only get a mid-level job. People with soft skills, like management and marketing, will be more impactful for the company. Around four years ago, Vladimir started with Kaggle. He needed to do something to help apply his skills. He compares working with Kaggle like working out at the gym. You lift weights, but you may not necessarily lift weights when you are outside of the gym. However, it does impact your appearance, strength, posture, and confidence. So, Vladimir started participating in Kaggle competitions and epically failing. Every competition helped Vladimir strengthen a particular skill. Machine learning is an applied discipline, Kaggle teaches this really well. 

Later, Vladimir touches on hiring data scientists. He says nobody knows how to hire. In Silicon Valley, so many people are competing. If you are performing well in the company, you get promoted. However, you can invest in looking around and finding another job where you can get a raise. These companies are also looking for an applicant with at least 10 years of experience when, in reality, they just want a nice person who can code. Stay tuned to hear Vladimir discuss machine learning, autonomous driving, and why he is not in a rush to pursue his own startup. 

Enjoy the show!

We speak about:

  • [02:00] How Vladimir started in the data space

  • [12:30] Transferring from academia to industry 

  • [21:40] Benefits of having soft skills   

  • [25:45] How Vladimir manages stress 

  • [31:30] Kaggle is like lifting weights 

  • [35:30] The hiring process for data scientists  

  • [40:45] Excitement for machine learning 

  • [46:00] Autonomous driving   

  • [47:55] Pursuing a startup

  • [51:40] Aiming to maximize mistakes in a day 

  • [61:00] Social life comes first 

Resources:

Vladimir’s LinkedIn: https://www.linkedin.com/in/iglovikov/

Kaggle: https://www.kaggle.com/iglovikov

Quotes:

  • “When you are coming to work at a startup, you have to wear a lot of hats.”

  • “On average, people work in Silicon Valley 1.5 years.”

  • “I encourage everyone to move to Silicon Valley for some time while you are young.”

  • “Building a startup is a rare and complex job.”

Books:

The 48 Laws of Power

So Good They Can't Ignore You: Why Skills Trump Passion in the Quest for Work You Love

The Making of a Manager: What to Do When Everyone Looks to You

Mastery

Now you can support Data Futurology on Patreon!  

https://www.patreon.com/datafuturology 

Thank you to our sponsors: 

UNSW Master of Data Science Online: studyonline.unsw.edu.au 

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au 

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Vladimir Iglovikov is based in San Francisco, California.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!



#59 Creating the Link Between Business and Data with Tony Gruebner - GM Analytics, Insights and Modelling

Tony Gruebner.jpg

Tony Gruebner is the GM Analytics of Insights and Modelling and the Exec Sponsor of Personalisation at Sportsbet. He established a department of 40+ skilled analysts and data scientists tasked with creating innovative data products focused at improving the experience for their customers and supporting the business by providing relevant and timely information and insights that steer decision making across all levels of the business. He has served on the Executive Leadership Team from 2016.

In this episode, Tony explains how he started in data and what led him to get his job at Sportsbet. Tony got a call from a recruiter asking if he wanted to do work with analytics, in a company that does sports and is heavily digital. All of those factors checked the box for Tony, and he took the entry-level analyst role. Over time, the need for analytics has grown, so he has been able to develop some analytics teams. 

One thing Tony does with Sportsbet is getting his data scientists to understand the link with the business, and the company to understand the connection with data science. The main strength of a data scientist is creating models; however, you need someone from the business side to tell them what kind of problems they need to be solved. Then, the data scientists can work on how to solve the issues numerically. Also, the business can consider things they currently cannot do, but data scientists can enable them to do these things. For Sportsbet, everything they do is to improve the customer experience.

Then, Tony explains how his team communicates how data science works. They actually painted out what the modelling cycle looked like and presented it to a lot of people. The feedback he got back is people understood the process and where the difficulties lie in the process. Communicating this always is challenging, especially when embarking on a project that may take six to twelve months to accomplish. Tony suggests breaking up the project into little chunks to avoid miscommunications. In one instance, after six months of work, the company explained the data was not solving the problem they wanted to be addressed. If Tony broke up the project into smaller pieces, this could have been avoided.

Later, Tony explains how Sportsbet is trying to scale globally and all the nuisances that come with the territory. For instance, they need to figure out how to acquire global talent and to overcome uncomplimentary time zones. Also, his team is working on how to utilize artificial intelligence to solve problems. Some of the models work on improving the customer experience directly. Just like how Netflix recommends movies, they are working on recommending a specific horse race to the customer; however, when the race is done, it needs to disappear from the site. Whereas, when Netflix recommends a movie to a customer, it can stay there for potentially years. Sportsbet is working on artificial intelligence to improve these models for their consumers. 

Enjoy the show!


We speak about:

  • [01:20] How Tony got started in data

  • [08:20] Tony’s skills come from the commercial side

  • [11:10] Linking data science and the business

  • [14:30] Communicating how data science works

  • [17:00] Steps to getting others to understand data science

  • [20:40] Getting the best talent for your team

  • [24:00] Structuring teams and the department

  • [28:10] Transiting from analytical roles to commercial roles 

  • [35:30] Working on global expansion

  • [38:10] Solving with artificial intelligence

  • [42:30] Passionate about using numbers to reach an outcome

  • [44:00] Modelling failures with Sportsbet 

  • [47:50] Imposter syndrome in data science  

  • [50:05] Data science is rapidly changing and exciting

Resources:

Tony’s LinkedIn: https://www.linkedin.com/in/gruebz/

Sportsbet: https://www.sportsbet.com.au

Tony’s Twitter: https://twitter.com/gruebz?lang=en

Quotes:

  • “There is no one path that always works.”

  • “There are literally thousands of things data scientists couldn’t potentially tackle in any business.”

  • “If you’re not making mistakes, then you aren’t pushing the envelope hard enough.”

  • “Not having imposter syndrome is a sign of lack of knowledge.”

Now you can support Data Futurology on Patreon!  

https://www.patreon.com/datafuturology 

Thank you to our sponsors: 

UNSW Master of Data Science Online: studyonline.unsw.edu.au 

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au 

Fyrebox - Make Your Own Quiz!

Tony Gruebner is based in Melbourne, Victoria, Australia.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

#57 How To Go From Academic to Data Science Leader with Yuval Marom - Analytics and Data Science Professional

Yuval Marom.jpg

Yuval is an Analytics and Data Science professional with extensive commercial and academic experience. His interests and goals are to be working on interesting and practical problems where there is a need to discover and act on meaningful patterns in data, through advanced analytics and data science. I'm the founder and co-organiser of two meetups: Data Science Melbourne and MelbURN, a user group for Melbourne-based users of the R statistical and data mining programming language. 

In this episode, Yuval tells us about how both of his parents are statisticians and inspired him to fall in love with data science. Growing up, he used Pascal to build spaceship games, and it motivated his passion for programming. Eventually, Yuval went for his Ph.D. and focused on applying how animals learn and behave to robotics. Simulated and physical experiments were pretty basic because robotics were not as advanced as they are today. Later, Yuval realized academia was not necessarily his calling, he was more interested in applying solutions to interesting problems. However, in recent years, research innovation and solving problems are becoming much more intertwined. 

Then, Yuval tells us why it essential to embrace simplicity and praises the advantages he reaped by working for a small business. In retrospect, Yuval realized he developed a higher level perspective of business level thinking by working for such a small team; however, he actually turned down a promotion because he was not yet confident in his technical skills. Get as close to the source of data as possible to get a real appreciation for it. Without appreciation, a person will not be successful in a data science managerial position. Doing technical work, Yuval was able to stay connected to the community and hear varying perspectives on how businesses operate and the different flavors of management. Later, Yuval explains his struggles with becoming a manager, the benefits of building connections in the workplace, and the importance of allocating time for professional development. 

 Enjoy the show!

We speak about:

  • [01:40] How Yuval fell in love with data science

  • [05:45] Social learning in biology

  • [08:05] Lessons learned from completing a Ph.D.

  • [13:10] Research innovation vs. solving problems

  • [15:40] Embrace simplicity 

  • [18:00] Small business advantages 

  • [21:45] Skills to develop before management 

  • [26:00] Results oriented work

  • [30:45] Different flavors of management

  • [32:50] Connection to community 

  • [40:20] Learning to interact with stakeholders + managerial skills 

  • [44:00] Benefits of building connections + education 

  • [48:00] Assume people are at work with good intentions 

  • [52:00] Allocate time for professional development 

  • [59:30] Focus on retention

Resources:

Data Science Melbourne 

MelbURN

Yuval’s LinkedIn

University of New South Wales

Tweetable Quotes:

  • “Embrace simplicity or go to academia.”

  • “You have to know the business process before you can make sense of the data.”

  • “Some people strive from innovation, whereas others find satisfaction from solving simple problems and making a difference in an organization.”

  • “If you have the right relationships, you can make anything work.”

  • “It’s important to try different experiences while you’re young, but facing the challenges where you are is equally important.”


Now you can support Data Futurology on Patreon!  

https://www.patreon.com/datafuturology 

Thank you to our sponsors: 

UNSW Master of Data Science Online: studyonline.unsw.edu.au 

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au 

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Yuval Marom is based in Melbourne, Australia.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!



#56 Every Business is an AI Business with Dr. Eric Daimler – Serial Entrepreneur, Technology Executive, Investor and Policy Advisor

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Dr. Eric Daimler is an authority in Artificial Intelligence & Robotics with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. Daimler has co-founded six technology companies that have done pioneering work in fields ranging from software systems to statistical arbitrage. Daimler is the author of the forthcoming book Every Business is an AI Business, a guidebook for entrepreneurs, engineers, policymakers, and citizens on how to understand—and benefit from—the unfolding revolution in AI & Robotics. A frequent speaker, lecturer, and commentator, he works to empower communities and citizens to leverage AI & Robotics. For a more sustainable, secure, and prosperous future.

In this episode, Eric explains how he has a vivid memory of getting a computer at the age of nine. He loves the machine, and even at such a young age saw the freedom a computer allows. Early in his career, Eric knew he wanted to work with brilliant and motivated people. When he was in New York, he saw the Netscape browser and instantly recognized the world was going to change. This inspired him to get out and find opportunities on the west coast.

Eric’s most significant failure as an investor was with a sports company. It was an idea of aggregating the worldwide demand for niche sports into an audience on the web that would allow for more significant marketing dollars. It was a fantastic idea and seemed like the appropriate time to go for it. One of his biggest takeaways from being an investor is that timing matters a lot. The bandwidth wasn’t there, so the experience ended up being quite weak.

There is a great deal of money looking to chase the next big thing. If you are looking for a house in a new city, and someone outbids you, you will lose the house. That doesn’t mean you aren’t going to move; it just means you need to start looking for a new home. If you receive a pitch and then some name brand firm takes it away from you, that doesn’t mean you can’t work in that field anymore. Now you have done your due diligence, you understand the market better and can look for other investment opportunities in the area.

AI is a system. We have to embrace this technology in its totality, the survival of our species depends on it. We have famously been able to survive to 2019.  There was a prediction back in the 1800s that we would have starved by now because the population was growing faster than our food production. Increase in productivity comes from technology and automation. When Eric is speaking, he likes to ask the audience what comes to mind when he mentions the term AI. People just don’t know what the word means. One of the critical issues that need to be addressed when companies employ AI is the recognition that our understanding of technology may change. Even the meaning of data has changed over the last year. Later, Eric explains how people have a long way to go regarding embracing AI, how technology is making driving easier, and AI in the medical field.


Enjoy the show!


We speak about:

  • [02:10] How Eric started in the technology space

  • [05:15] Moving from one career path to another

  • [09:50] Eric’s most significant failure as an investor

  • [13:30] Picking the timing  

  • [18:15] AI is larger than what currently exists  

  • [21:30] Embracing the technology behind AI

  • [29:45] Hurdles for companies who are adopting AI  

  • [41:30] Reactions from people learning about AI

  • [48:40] Shortage of truck drivers + how technology is making driving easier

  • [54:00] AI in the medical field

  • [61:30] Using a categorical approach  


Resources:

Eric’s LinkedIn: https://www.linkedin.com/in/ericdaimler/

Eric’s Twitter: https://twitter.com/ead

Website: http://conexus.ai/


Quotes:

  • “Most people get the timing very wrong.”

  • “I am starting to look at investments where others are not looking.”

  • “I wanted to change the Hollywood narrative around AI.”

  • “We were naive about our privacy in 2016.”


Now you can support Data Futurology on Patreon!  

https://www.patreon.com/datafuturology 


Thank you to our sponsors: 

UNSW Master of Data Science Online: studyonline.unsw.edu.au 

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au 

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Dr. Eric Daimler is based in San Francisco, California.


And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!



#55 Martin Ford – Author and Futurist

Martin Ford.jpg

Martin Ford is a prominent futurist, New York Times bestselling author, and leading expert on artificial intelligence and robotics and their potential impact on the job market, economy and society. His 2015 book, "Rise of the Robots: Technology and the Threat of a Jobless Future" won the Financial Times and McKinsey Business Book of the Year Award and has been translated into more than 20 languages.

In this episode, Martin discusses his best-selling books and describes some of the themes he writes about. For instance, in Rise of the Robots he talks about “The Triple Revolution” which was a report presented to U.S. President Lyndon B. Johnson fifty years ago that argued this would be a dramatic change to the economy; however, it never really panned out. Martin’s argument for artificial intelligence started back in 2009 after writing his first book titled The Lights in the Tunnel. Ultimately, artificial intelligence will become so powerful that it can have a significant impact on employment that will compete with a large fraction of the workforce.

Then, Martin discusses the impact of jobs, the economy, privacy, and democracy with the influx of automation. There was a recent announcement from OpenAI stating they have created a sophisticated deep-learning system that was able to generate narrative content. In other words, the system can create reviews, articles, and poetry proving AI can be creative. The company withheld the technology because of fear people would use the system to turn the internet into garbage full of fake news and fake reviews.

Later, Felipe and Martin discuss the most common occupational error, driving. Self-driving cars will threaten Uber drivers, taxi drivers, and truck drivers. It may take a bit longer than some people are saying, the technology will be coming within ten to fifteen years. Martin believes the hardest jobs to automate are in three categories: genuine creativity, relationship-building, and skilled trade jobs. We put so much emphasis on going to college; however, the safest jobs are going to be the electrician and plumber. Many people do not thrive at university, half of them are not finding jobs that leverage their education after graduation. Then, Martin explains universal income; giving everyone a minimal level of income to allow them to survive in the absence of a traditional job that would provide money. Stay tuned to hear Martin discuss deep learning, data banks, and the negative implications of artificial intelligence.

Enjoy the show!

We speak about:

  • [02:50] Martin’s background

  • [05:45] The themes behind Martin’s writing

  • [08:35] Machine learning is when algorithms can make decisions  

  • [12:00] Amazon is susceptible to automation

  • [16:45] The most common occupation error is driving some kind of vehicle   

  • [18:15] The type of work that will be left for humans  

  • [21:45] Universal basic income  

  • [28:55] Building explicit incentives to earn more income; paying people more to pursue education

  • [33:25] Artificial intelligence will be the primary force shaping our futures

  • [38:35] The solution is not to teach everyone how to code

  • [41:30] Architects of Intelligence: The truth about AI from the people building it

  • [46:00] Deep learning is the biggest thing to happen to artificial intelligence  

  • [52:20] Controlling data and an entirely new industry called data banks

  • [53:15] Negative implications of artificial intelligence

  • [64:40] You do not want to be doing something predictable

Resources:

Martin’s Website: https://mfordfuture.com/about/

Martin’s LinkedIn: https://www.linkedin.com/in/martin-ford-5a70428/

Martin’s Twitter: https://twitter.com/MFordFuture

TED Talk: https://www.ted.com/talks/martin_ford

Books:

Rise of the Robots: Technology and the Threat of a Jobless Future

Architects of Intelligence: The truth about AI from the people building it

The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future

Quotes:

  • “It would be a huge mistake to assume only blue-collar workers will be impacted.”

  • “Education is not going to be enough in the long run.”

  • “The last thing we need is a dumbed down population and less informed voters.”

  • “I believe artificial intelligence will be the best thing to happen to humanity.”

Now you can support Data Futurology on Patreon!  

https://www.patreon.com/datafuturology 

Thank you to our sponsors: 

UNSW Master of Data Science Online: studyonline.unsw.edu.au 

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au 

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Martin Ford is based in Sunnyvale, California.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!


#54 Annie South – General Manager, Data

Annie South.jpg

Annie South is the General Manager of Data at ME Bank. She is an Information Management professional with twenty years’ experience of complex information environments spanning the full spectrum of structured data to unstructured information. Annie has in-depth technical knowledge of various specialisms, including metadata, data warehousing, data governance, data quality, enterprise architecture, data lineage, Big Data, data analytics, and regulatory requirements.

In this episode, Annie explains the things she does to ensure her career is future ready because nobody can predict what jobs will look like years from now. Do not specialize in a particular technology but specialize in a capability. The technologies that you are using today will not be the technologies they are using tomorrow. If you specialize in a particular technology set, and it is decreasing in popularity, you will end up with fewer opportunities in the market. Annie tells people wanting career advice that when people look at your resume, they are looking for a consistent arc. That could mean staying consistent in an industry or constant engagement in the workforce. Another thing Annie looks for in applicants is kindness, this quality is something that cannot be taught.

To have any sort of success in an organization, you need to have immersed yourself in their environment for a significant amount of time. It has taken Annie a lot of time to grasp her organization’s culture and be able to manage her team efficiently at ME Bank. Annie refers to her team as the librarians, and data scientists are the people who come and read the books. Data is all about human beings being paired with information and knowledge; it helps people live better lives by pairing them with relevant and essential tools. Annie strongly encourages data scientists to follow their passions and continue to look at different opportunities in the field. Later, Annie discusses diversity in the workforce, overcoming discrimination in the workplace, and explains the importance of LinkedIn for social networking.

Enjoy the show!

We speak about:

  • [01:20] How Annie got into the world of data

  • [10:00] Insight for people starting in the data space

  • [12:50] Organizations are not predictable  

  • [14:50] Annie’s team at ME Bank

  • [27:50] Turning recruitment on its head

  • [33:20] Transitioning from teaching to general manager

  • [39:05] Sort out your personality and experiment with leadership

  • [46:30] Imposter syndrome  

  • [49:10] Experimenting with diversity in the workforce

  • [53:30] Challenges with discrimination in the workplace   

  • [61:10] Define yourself; do not be defined by others

Resources:

Annie’s LinkedIn: https://www.linkedin.com/in/annesouth/

ME Bank: https://www.mebank.com.au

IT Jobs Watch: https://www.itjobswatch.co.uk


Quotes:

  • “You are not going to be able to predict the roles that will exist in twelve months.”

  • “Sometimes, a job is better than no job.”

  • “Organizations are extremely complex, quite random chaotic things.”

  • “You will never get ahead by being excellent at what everyone else is doing.”

Now you can support Data Futurology on Patreon!  

https://www.patreon.com/datafuturology 

Thank you to our sponsors: 

UNSW Master of Data Science Online: studyonline.unsw.edu.au 

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au 

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Annie South is based in Melbourne, Australia.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!