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

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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!



Annie South – General Manager, Data

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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!


Pavel Pleskov – Data Scientist & Kaggle Grandmaster

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Pavel Pleskov is a data scientist at Point API (NLP startup) and currently ranks number 3 out of 109,624 on Kaggle, making him a Grandmaster. Pavel has started companies in the past and has worked in many different industries before becoming a data scientist and Kaggle Grandmaster.

In this episode, Pavel explains his background and how he started in the data science space. When Pavel’s girlfriend went to pursue her master’s degree in London, Pavel began interviewing for a quantitative research job nearby. Turns out, the company was a rival of his current employer, causing him to get fired from his job the next day. Former employees of this job contacted Pavel to ask if they would join their new trading firm and be head of their research team. After doing his job for two years, Pavel knew he was capable of doing it on his own. The company works remotely, and after spending time in bitter Russian winters, Pavel looked to work elsewhere. The ideal country turned out to be Vietnam and was Pavel’s first time outside of Russia.

Then, Pavel started taking machine learning courses and found the platform Kaggle to practice what he has learned. Practically, Kaggle is much more helpful than homework or any online course. The most significant part of his success was spending full-time on Kaggle rather than working another job. Then, Pavel explains the difference between machine learning engineers and researcher data scientists. Later, Pavel reveals his intention behind becoming the very best at his work. The key to success is to find something you like and become the very best at what you are doing. If you are doing something that you do not enjoy, then there is no way you will become the very best. The only way to overcome your obstacles is to enjoy what you are doing.


Enjoy the show!


We speak about:

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

  • [09:50] Vietnam is an ideal space for working remotely and teaching English

  • [21:40] The moment Pavel found Kaggle

  • [24:20] How Pavel became a data scientist

  • [28:00] Difference between machine learning engineers and researcher data scientists

  • [31:50] Why is it essential to be the very best?

  • [34:20] Machine learning and mathematics

  • [36:45] The early days of Pavel’s Kaggle journey

  • [40:00] Pavel’s favorite part of Kaggle

  • [47:20] The role of automation in Kaggle   

  • [49:40] The steps when approaching a new Kaggle competition

  • [52:55] Think twice before you commit to data science


Resources:

Pavel’s LinkedIn: https://www.linkedin.com/in/ppleskov/?originalSubdomain=ru

Pavel’s Kaggle: https://www.kaggle.com/ppleskov

Pavel’s Twitter: https://twitter.com/ppleskov

Quotes:

  • “It is tricky to work remotely as a data scientist.”

  • “It takes a lot of time to find your passion in life. People spend too much time on things they do not enjoy.”

  • “When I first started Kaggle, I entered any competition and tried to improve my score on the leaderboards.”


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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Pavel Pleskov is based in Moscow, Russia.


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



Danielle Timmins – Chief Data Analytics Officer

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Danielle Timmins is the Chief Data Analytics Officer for Free Range Creatives. Free Range Creatives is a digital marketing agency that is deeply rooted in data and analytics. They have a different view on agency life and challenge the existing ways of working. They believe that work should be fun (well, at least most days) and that our work must be insightful, inspirational and effective.

In this episode, Danielle tells us how she did not start in the data space but initially wanted to be a doctor. Danielle ended up getting a Master’s in Economic Psychology, during which she concentrated on the digital side of marketing. This is where Danielle got her exposure to data and started to understand it. Danielle got her first start at an NGO in a marketing position. She would shoot mini-documentaries for television and then moved into a more traditional marketing role. Danielle’s first job as a strategist was down in South Africa where she worked with several different clients. This is when she would start to work with data and incorporate it with strategy.

Danielle says that her most significant learning curve has been communicating related data information to people that do not understand data. She recalls how she was in a meeting and wondered what to do with the numbers that were presented and challenged people on what the data meant and how it was presented. Danielle says that these questions were not really asked at the beginning and said that they only came about once she got into a higher position. She loved sharing how she got to the data and the process, but she learned that most people do not. This opened her eyes to make sure that data is broken down into something exciting and easy to understand.

Later, Danielle explains how her style is more visual than number driven or a standard PowerPoint, she uses a TED Talk presentation. This method utilizes visuals that create excitement; the brain works better with visuals. Usually, there are specific questions that we are trying to answer, and we can help answer these questions with visual stories. There are multiple presentations for different audiences, and you must know this to tell the visual story.

Enjoy the show!

We speak about:

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

•    [03:20] Background and career  

•    [06:20] Deciding what problems to tackle first on the job

•    [08:35] Evolution of marketing   

•    [13:35] Favorite failure

•    [16:50] How to communicate data

•    [18:30] Visual presentation style

•    [19:45] How Danielle creates a story

•    [21:30] How do you structure visuals for executives?

•    [23:10] How do you think people can get better at this skill?

•    [24:45] What is a strategist for data?

•    [27:40] What is the role outside of data?

•    [29:00] The main challenges for Danielle’s clients

•    [32:30] Working with clients on case-by-case basis

•    [33:30] Qualities of a great data scientist  

•    [35:30] What do you think makes a good data leader?

•    [36:15] Current challenges in the data space

•    [37:40] Future challenges for the data space

•    [42:40] Advice for future data scientists and leaders

Resources:

Sexy Little Numbers

Free Range Creatives: https://www.freerangecreatives.co.za/

Danielle’s LinkedIn: https://www.linkedin.com/in/danielletimmins/

Quotes:

•    “There are many different audiences, and you must adapt your story for each one of them.”

•    “Data can sometimes be abused during storytelling, but I hope that over time, this will change and only be used to help support someone.”

•    “All audiences need to be comfortable with what we are doing as data analysists and sharing their work.”

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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Danielle Timmins is based in Essen Area, Germany.

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


Peter Elger – Founder and CEO

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Peter Elger is the founder and CEO of four Theorem; his focus is on delivering business value to his clients through the application of cutting edge serverless cloud architectures and machine learning technology. His experience covers everything from architecting large-scale distributed software systems, to leading the internationally-based teams that built them.

In this episode, Peter tells us how his first real passion was in physics. After graduating with a BSc in Physics and a master’s degree in Computer Science, he worked for several years at the Joint European Torus (JET), the world's largest operational magnetically confined plasma physics / nuclear fusion experiment. They were doing big data, but at the time they did not refer to it as such; they dealt with around four to five terabytes of scientific data. Peter then transitioned to Indigo Stone as a Senior Technical Architect. Indigo Stone was a software disaster recovery firm which exited in 2007 to EMC.

Peter explains how it is essential to keep your technical skills up-to-date and why some of his favorite days are when he gets to code despite being the CEO of his company. If you can actually be the bridge between the business and the technology, you are an invaluable asset to any company. The freedom to innovate is what led Peter to his entrepreneurial ventures; previously, he had no real experience being his own boss. Peter says it is dangerous to think you can do everything; you have might a broad skill set, but you need to recognize that you have gaps. This is why Peter has always started businesses with co-founders. Currently, his co-founder is a world-class technologist and someone who understands the human dimension. All of his current co-founders and people Peter has worked with previously.

Later, Peter explains why he wrote about applying AI to existing platforms in his book, AI as a Service. He also recently wrote a chapter on how to apply machine learning to existing systems and the patterns that arise within two systems. Then, Peter describes why you do not need knowledge of AI to use AI as a service. In the past, everyone wanted their own security department; however, people already have the same technology that is vastly better than your own. This applies to how you can utilize AI within your existing business systems. Peter wants people to be interested in machine learning, but only a basic understanding is essential to use it within a business context. Finally, Peter discusses his approach to finding the most capable team members, being open and upfront with all staff members, and removing the ego to allow his team to perform their best work.


Enjoy the show!


We speak about:

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

  • [06:50] Transition to disaster recovery  

  • [08:55] Interactive radio and marketing applications

  • [13:40] Maintaining a grip with technical skills

  • [16:20] The entrepreneurial bug came organically to Peter

  • [18:40] Transition to entrepreneurship  

  • [21:45] What to look for in a co-founder

  • [26:00] Building analytics with machine learning

  • [29:00] A tale of two technologies

  • [33:00] Applying AI to existing platforms

  • [35:10] Knowledge of AI is not necessary to use AI as a service  

  • [37:50] Capable team members are difficult to find

  • [40:10] Sharing management meetings with all staff members  

  • [44:05] Experiences with handling politics in organizations

  • [48:50] Removing ego + allowing the team to do their best work

  • [50:30] Scheduling work to maximize the impact


Resources:

Peter’s LinkedIn: https://www.linkedin.com/in/peterelger/?originalSubdomain=ie

Peter’s Twitter: https://twitter.com/pelger

Peter’s Book: AI as a Service


Quotes:

  • “Computers don’t lie, and they do what you tell them.”

  • “In order to sell to someone, you need to understand what their problems are.”

  • “Business is all based on delivering value and building relationships.”

  • “Good software engineers have been doing things before they had a name.”


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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Peter Elger is based in Ireland.



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



Prakash Baskar – Founder and President

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Prakash Baskar is the Founder and President of Khyanafi. He helps data leaders to rapidly transition and accelerate the success of data, analytics, and digital initiatives. Previously, Prakash was the Chief Data Officer at Santander Consumer USA where he led enterprise data governance, risk infrastructure & information (risk data aggregation), data quality, business data strategy & solutions, and business & reporting analysis functions.

In this episode, Prakash tells us how he started in the data space at his university. His role was to determine how students were performing. If they are not performing well, he needed to identify why. The graduation rates were low at the school, so Prakash was tasked with finding out what was the problem. Then, Prakash discusses starting a new job and having little direction about what to do. With everchanging technology, the description of your job will always be changing too. As a person going into any role, understand that you do not have to ask permission all the time. Have a clear idea of what you can do and what you cannot do, then do what you feel is right for the organization. Look for where the opportunities for expansion are and find a way to get results.

If you ask ten people what the role of a Chief Data Officer is, you will get ten different answers. Whatever the CDO does will ultimately be to enable others to receive real benefits out of the data. Just because something is not broken, does not mean it cannot be improved. There are many different routes a person can take to become a CDO; however, you need someone with knowledge in multiple aspects of business, technology, and people management. A CDO needs to create value for the organization; learn the company you are supporting to anticipate the problems they may run into.

Later, Prakash explains how in business, any change is hard. How you embrace the change after it is made is what will differentiate yourself from others. If the change is too complicated, people will shut off. Start off by telling the client what the change will do for them rather than the steps it will take to get there. Some other tips when presenting a significant change is to be realistic with what it will take and make sure not to overpromise. It is imperative to select things that you can quickly do with minimal engagement from their people. Plus, make sure you have updates for the company each month, so they understand what is being revealed from the data. Finally, Prakash discusses how essential it is to move around the organization in order to understand different departments and he reveals the inspiration behind his latest business venture.

Enjoy the show!

We speak about:

  • [01:30] How Prakash started in the data space

  • [03:55] The transition into consulting  

  • [07:50] Deciding what problems to tackle first on the job

  • [10:10] The role of Chief Data Officer  

  • [17:20] Creating value for the organization  

  • [22:10] Businesses getting the maximum benefit from analytical work

  • [31:25] How to determine what to work on first with a company

  • [39:00] Data science conferences are full of CDOs

  • [45:45] Actively moving around the organization

  • [50:20] The inspiration behind Khyanafi

  • [53:50] What do you think makes a great leader in the data space?

  • [55:40] Advice for data scientists   

Resources:

Prakash’s LinkedIn: https://www.linkedin.com/in/prakashbaskar/

Khyanafi: http://www.khyanafi.com

Khyanafi’s LinkedIn: https://www.linkedin.com/company/khyanafi/about/

Set up a discovery call with Prakash: https://calendly.com/prakashbaskar/discoverycall

Quotes:

  • “Your job description is only as good as the time it was written.”

  • “I am not a big believer in five or ten-year plans; I make plans six months at a time.”

  • “CDOs have to know the business! Try and learn how the company operates; you will gain more respect when you take the effort to understand the business.”

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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Prakash Baskar is based in Columbus, Ohio.


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

Valeriy Babushkin – Head of Data Science

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Valeriy Babushkin is the Head of Data Science at X5 Retail Group where he leads a team of 50+ people (4 departments: Machine Learning, Data Analysis, Computer Vision, R&D) and increases profit in a 25+ billion USD company. Also, Valeriy is a Kaggle competition master; ranking globally in the top 60.

In this episode, Valeriy explains his background and how he started in the data science field. At one point, he received an offer for a senior position at a bank; it was the largest privately owned bank at that time in Russia. Valeriy did not find out that he was doing machine learning until working on it for two years. What someone is doing right now could be pretty close to machine learning, and they don't even know. Then, Valeriy speaks on how trust is essential to the job of a data scientist; not only between you and your boss but between you and other departments. Trust will make your job easier when explaining the data, the results, and how reliable they are for the company. However, if there is an existing data science department in the company, you will not have to work as hard to earn the trust of others because it already exists. Sometimes when data scientists join a company, they think their job will just be to code all day. That is not always the case, you will have to talk to many people and often be a business analyst.

Next, Valeriy discusses setting up teams in the data science space and how many people really need to be involved. For instance, if an algorithm is your product, you will need not only data scientists but product managers, project managers, and software engineers. If you are building the data science department, what do you need to grow? You will need to build a roadmap for the product and know how you want the company to improve. Then, Valeriy explains why reliability is one of the most essential qualities of an employee. For instance, if you have a critical task would you rather give it to an employee with a fifty percent chance of completing it in two weeks or someone who has a ninety-nine percent chance of handling it within six weeks? You will give it to the person that is more reliable, despite the fact it may take them a little longer to complete it. Later, Valeriy reveals the story behind his Kaggle journey and discusses some ethical challenges in the data science industry.

Enjoy the show!

We speak about:

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

  • [06:10] Transiting to working at a bank

  • [11:30] Understanding the business process

  • [15:10] Gaining trust from clients

  • [20:20] Data scientists are business analysts

  • [24:10] Expectations from the job interview

  • [25:50] Starting data science teams

  • [31:40] The type of mindsets to look for in a team member

  • [37:30] Different teams complement each other

  • [40:20] Valeriy’s journey with Kaggle

  • [47:40] Ethical challenges in the industry

  • [51:20] Persistence is key

Resources:

Valeriy’s LinkedIn: https://www.linkedin.com/in/venheads/

Valeriy’s Kaggle: https://www.kaggle.com/venheads

Quotes:

  • “It makes sense to have a job interview which is pretty close to your daily routine, your daily work.”

  • “Before hiring new people, you have to make a path.”

  • “One of the most important qualities for employees to have is reliability.”

  • “It is impossible to work directly with two people if you just spend five minutes a day with each of them.”


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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Valeriy Babushkin is based in Zug, Canton of Zug, Switzerland.



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



Jay Liu - Chief Data Scientist at Digital-Dandelion

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Jay Liu is the Chief Data Scientist at Digital-Dandelion specializing in helping insurance, and medical organizations innovate by integrating the latest in Artificial Intelligence (AI), machine learning and big data into their systems. Knowing the best way to learn is by putting your money where your mouth is, Digital-Dandelion launched an online brand and built a customer AI to promote it. There were numerous technical and modeling challenges that were overcome, but in the end, they sold all their stock within three months. They had proven to themselves that customer AI worked. Organizations can have great depth and breadth of customer data from their long-term relationships of selling high-value products and services.

In this episode, Jay explains how he found himself in advertising and started getting fat because of all the Michelin star restaurants his potential clients would treat him to. His data science career began with loyalty cards and being incredibility confident. When someone uses a loyalty card, the company is collecting data. They will know exactly what you purchased and how much you purchased of each item. The customer will be rewarded with monthly coupons. Jay was in charge of coming up with the coupons that were designed to make the customer spend more money in the store. Knowing at least one data programming language will leverage what you have and give you one foot in the door. The best way to get into data science is to know how it will improve the current industry or business you are working for.

Later, Jay explains why QA is a lost skill and the idea that great data scientists have internal discipline. However, there is a race to push the boundaries and become more automated. For example, Facebook collects as much data as possible and thinks about the consequences later. Data is data and people are people. Understanding data is the starting point. Before Jay starts a job, he dives deep and analyzes what every number means to the business with their data collection. Also, Jay considers how to make his bosses job as easy as possible. Overall, the success of his boss will create the most significant impact on his business. If someone has been working at the same job for ten years, they are scared to grow and try something new. Finding a data scientist who has worked at multiple different sizes and types of organizations is the key to finding a well-rounded employee.

Enjoy the show!

We speak about:

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

  • [06:15] Loyalty cards

  • [08:50] QA is a lost skill

  • [10:25] You are your own police

  • [13:30] Ethical considerations

  • [15:10] Transition from marketing to data science

  • [20:40] Putting yourself on the line for the benefit of the company

  • [22:15] Creating change in organizations

  • [27:30] Learning new applications + algorithms

  • [33:50] What makes a great data scientist?

  • [38:15] Delivering results to alleviate pressure

Resources:

Jay’s LinkedIn: https://uk.linkedin.com/in/jay-liu-76ab2b8a

Digital-Dandelion: https://www.digital-dandelion.com

Quotes:

  • “If you want to get into data science, the best way to do it is to understand how data science can help your current industry.”

  • “I have actually never worked as a full-time employee for more than two years.”

  • “If you want to change, you need to be ready to fight for it.”

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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Jay Liu 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!

Marek Rucinski – Deputy Commissioner, Smarter Data Program

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Marek Rucinski is the Deputy Commisioner leading the Smarter Data Program at the Australian Taxation Office (ATO). Marek has taken part and driven the evolution and transformation of Marketing, Analytics, Data and Digital capabilities for over 20 years. This has been done in both industry roles and consulting services capacity, across Australian, Asian and Global clients, across Retail, Telco, Consumer Goods, Financial Services, Mining & Utilities sectors. His passion centers on helping clients change the role of Marketing & Analytics capabilities in Digital and Data age, from activating the capability through acting on insights, to transforming customer experience and the whole business via delivering value across business functions. Prior to ATO & Accenture, Marek lead and created analytics functions and teams in a Retail industry, and developed global corporate strategy frameworks and analytics in a multinational organizations.

In this episode, Marek tells us about how he was always interested in the science behind marketing. Marketing as a discipline has been completely transformed due to the emergence of data as a driver for engagement with the customer. Marek is not a classically trained data scientist; he is a data strategist and can dive deep into the organization’s needs in order to drive value to the customer. Marek tells us how some businesses can struggle with how to handle the findings of research from data scientists. It is essential to translate the potential into targets to create the prize. Leave the ego at the door and find the ability to be critiqued.

Later, Marek tells us how educating businesses on analytics as a mechanical process is essential for them to perceive how the whole thing works. He then explains his transition from consulting to government and how his excitement lies in the play with analytics at an enormous scale. Then, Marek describes how to have each section of the value chain working with purpose and precision. Data has to be trusted, organized, and accessible for the company. A data strategist must consider how the data is being delivered to their client. You want to create products and interactive experiences for the business as opposed to simple spreadsheets. Finally, Marek answers the audience’s questions including what makes a good data scientist and current challenges in the data science industry.

Enjoy the show!

We speak about:

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

  • [05:25] Activating the value from data

  • [08:30] POCs are essential  

  • [09:45] Find people who can create the vision

  • [12:00] Educating businesses on analytics

  • [16:30] Artificial intelligence + automation

  • [18:50] Transition from consulting into government

  • [20:20] Motivations for government work

  • [22:00] Future of ATO

  • [26:15] Continuous production of insights

  • [29:30] Audience questions  

Resources:

Marek’s LinkedIn: https://www.linkedin.com/in/rucinskimarek/

Quotes:

  • “Good results create more interest which in turn creates traction for new products.”

  • “If you engage the business regarding the value, but then you cannot deliver on the promise, it creates dissonance.”

  • “What separates great data scientists is their ability to communicate what the results actually mean.”

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Marek Rucinski is based in Sydney, Australia.


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