#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!

#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!


#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!



#67 12 Questions to Ask in Mentoring Sessions with Felipe Flores – Founder & Podcast Host

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In this episode, Felipe talks about an aspect of leadership, the one-on-one mentorship and feedback session with people on your team. To start, ask your team member what is on their mind and in general, how have things been going? If they do not have anything pressing they want to discuss in the sessions, Felipe turns to set of twelve questions. The first question is what you are most proud of that you have done since we last caught up? These questions are designed as tools for the team member to recognize the need for self-assessment. What they say is not essential; really, the follow-up questions are more necessary to help them uncover themselves. Next, ask what the team member could have done better since the last time you talked. By allowing them to evaluate and think of improving continually, they can learn faster and become more efficient in their work. 

The next few questions require a broader perspective and ask about the team as a whole. It should be clear that everything is a team effort, and all member’s ideas are respected and heard by others on the team. After the team questions, head back to questions about the person and ask what they would like to work on or improve? Then, the next issue will take a lot of trust and rapport with your team member, ask what is one thing that is true that you think I do not want to hear? Question nine is how I can help you to do better? This question has taught Felipe that he is good at the big picture but needs to focus on the details and how the team might achieve the big picture. Finally, the last three questions are asking what they like best and least about the organization and if they are happy at the moment.

Enjoy the show!

We speak about:

  • [01:35] Open-ended questions

  • [02:15] What are you most proud of that you have done since we last caught up?

  • [03:45] What could you have done better?  

  • [05:30] Questions about the team

  • [10:00] What would you like to work on or improve? 

  • [11:40] What is one thing that is true that you think I do not want to hear?

  • [13:10] How can I help you to do better?

  • [15:00] What do you like the least about the team? 

  • [16:00] What do you like best about the team?  

  • [16:35] Are you happy at the moment? 

Resources:

Saturday Night Live

Quotes:

  • “One of the best and quickest ways to learn is to evaluate your efforts continually.” 

  • “Understand the human behind the data scientist.” 

  • “You don’t need to be fixing every person’s problem, but everyone needs help every now and again.”

Thank you to our sponsors:

Fyrebox - Make Your Own Quiz!

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

Felipe Flores 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!

#63 Set Yourself Multi-Year Professional Challenges with Felipe Flores – Founder & Podcast Host

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In this episode, Anthony Ugoni, one of Australia’s more prominent leaders in analytics interviews Felipe. Felipe came to Australia as a backpacker and ended up falling in love with the place. With Spanish as his first language, the only English he could say was the jacket is black. Then, Felipe explains some of his odd jobs and working freelance IT. At university, Felipe wanted to specialize in data, but all of his friends told him it was dead. So, he ended up specializing in hardware, even though all of his work was in data. When Felipe went to do his thesis, he happened to stumble into a project involving brain wave activity. The electrical engineer did all the research and design, the signals would be passed to Felipe’s computer, where he made his first application of machine learning. 

Then, Felipe explains how he and a colleague of his made the decision to quit their jobs at a small consulting firm. They decided to start their own firm, despite knowing very little about business. The first year they almost went bankrupt about four times and made lots of mistakes. They wanted to be in analytics but were unsure how to sell their services. The two spent six months creating a piece of software. When they went to show prospects they found out people did not like the entire product. So they decided to focus on their consulting business. 

After gaining so many clients, Felipe was literally sleeping in the office and billing over 100 hours each week. Coming up with solutions for the customers needed to be part of the sales process. Do exactly what the customer needs, and do it very quickly to build trust. Although you may be able to do even more for the customers to advance their businesses, target their needs first. After three trusted transactions with the customer, your relationship is built on trust, and then you can start recommending different projects. Eventually, Felipe went back to some of the people who expressed interest in investing in the company over the years and said they had an opportunity to invest. His partner got 51%, and Felipe sold his shares, the company is still going and has been rebranded. Later, Felipe discusses his work as the Executive Director & Head of Data Science at ANZ and explains their supportive work culture. Felipe also reveals the inspiration behind his podcast, Data Futurology, and describes his excitement behind explainable AI. 

Enjoy the show!

We speak about:

  • [02:40] Felipe’s background 

  • [06:10] Education and specializations

  • [14:30] Quick delivery of value  

  • [17:20] A series of odd jobs and IT freelancing  

  • [24:20] Setting up his own consulting company  

  • [33:15] Highs and lows of Clear Blue Water

  • [37:30] Executive Director & Head of Data Science at ANZ

  • [47:55] Supportive and open culture at work 

  • [52:40] Understanding the business at a new job

  • [54:45] Inspiration behind Data Futurology

  • [62:00] Explainable AI 

Resources:

Felipe’s LinkedIn: https://www.linkedin.com/in/felipefloresanalytics/?originalSubdomain=au

Episode #21 Antony Ugoni: https://www.datafuturology.com/podcast/21

Quotes:

  • “If I’m an engineer, people will think I’m smart.”

  • “A colleague of mine and I decided to set up our own consulting company. Professionally, it was the best and worst thing I’ve ever done.”

  • “Sales is built on trust and a human connection.”

  • “I had not done a good job of being a leader and creating a culture.”

  • “How can we make data scientists today, the CEOs of tomorrow?” 

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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Felipe Flores 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!