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 


Oliva’s Website:

Data Science Consulting:

 My Big ‘Why’ -

LOVE@WORK now available at - A Silver Nautilus Book Award-Winner 

The LOVE@WORK MethodTM now available at

What is your Corporate Love Quotient? Find out here   

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

Oliva’s Social Media:



Twitter handle: #OliviaParrRud  


Instagram: Love.MakeItYourBusiness


  • “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:

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Datasource Services: or email Will Howard on

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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, 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 


Prashant’s LinkedIn:

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


  • “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!

Thank you to our sponsors:

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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!

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


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 


Felipe’s LinkedIn:

Episode #21 Antony Ugoni:


  • “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!

Thank you to our sponsors:

UNSW Master of Data Science Online:

Datasource Services: or email Will Howard on

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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!

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

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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


Greg’s LinkedIn:



  • “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!

Thank you to our sponsors:

UNSW Master of Data Science Online:

Datasource Services: or email Will Howard on

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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


Sveta’s LinkedIn:



  • “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!

Thank you to our sponsors:

UNSW Master of Data Science Online:

Datasource Services: or email Will Howard on

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


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 


Vladimir’s LinkedIn:



  • “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.”


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


Now you can support Data Futurology on Patreon! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

Fyrebox - Make Your Own Quiz!

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

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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


Tony’s LinkedIn:


Tony’s Twitter:


  • “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! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

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!

#58 Explainable AI by Felipe Flores

Felipe Flores.jpg

Today we have a different type of episode, this is a presentation that Felipe did at the Chief Data and Analytics Officer Conference in Canberra, and it is on explainable AI. First, Felipe explains how Amazon used a secret AI recruiting tool that had a bias against women. Also, the U.S. government used an algorithm predicting how likely people in the criminal justice system would reoffend. What they found is that it targeted specific racial groups. The algorithm isn’t racist or sexist, the data is.

Regarding job applications, as your company scales up, the need to automate the process of looking at the applications becomes necessary. Sometimes, bias will creep into the automated decision-making algorithm. The bias can even be narrowed down to the person’s name. For example, somebody with name Felipe might get scored lower than somebody with the name Tyler. Lean into the inequality and predict the bias. You can plug in the CV information, and ask the algorithm to predict the person’s race and gender. Then, find out what key inputs they are flagging to determine this and remove them from the algorithm.

Then, Felipe explains how algorithms can tackle unstructured data approaches. When discussing images, an algorithm was able to correctly identify a wolf from a husky 5 out of 6 times. However, when uncovering how the algorithm determined which was which, it was merely looking at if the animal was in the snow or not. If the picture had snow in it, then it must be a wolf. To determine how this algorithm was functioning, Felipe used LIME - Local Interpretable Model-Agnostic Explanations. It works for classifications and came out of a study from MIT. Later, Felipe discusses using EL15 and how transparency is essential for the public to understand how the algorithms could affect them.

Enjoy the show!

We speak about:

  • [03:40] Large companies and their biases

  • [05:40] Racism and sexism is in our data

  • [08:45] Uncovering inputs of the bias

  • [10:45] Unstructured data approaches

  • [14:30] Using ELI5

  • [19:20] The right to an explanation


  • “We teach our algorithms on how to replicate our decisions.”

  • “The algorithms show the inequality that we have in the world today.”

  • “Explainable AI is more ethical in the sense that it is more transparent.”

  • “Explainable AI helps us avoid blunders and informs us how the algorithm perceives the data.”

Now you can support Data Futurology on Patreon! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

Fyrebox - Make Your Own Quiz!

Felipe Flores 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


Data Science Melbourne 


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! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

Fyrebox - Make Your Own Quiz!

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

Eric Daimler.jpg

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  


Eric’s LinkedIn:

Eric’s Twitter:



  • “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! 

Thank you to our sponsors: 

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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

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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


Martin’s Website:

Martin’s LinkedIn:

Martin’s Twitter:

TED Talk:


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


  • “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! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

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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

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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


Annie’s LinkedIn:

ME Bank:

IT Jobs Watch:


  • “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! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

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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!

#53 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


Pavel’s LinkedIn:

Pavel’s Kaggle:

Pavel’s Twitter:


  • “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! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

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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!

#52 Influencing Marketing with Data Science with Danielle Timmins – Chief Data Analytics Officer

Danielle Timmins.jpg

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


Sexy Little Numbers

Free Range Creatives:

Danielle’s LinkedIn:


•    “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! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

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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!

#51 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


Peter’s LinkedIn:

Peter’s Twitter:

Peter’s Book: AI as a Service


  • “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! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

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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!

#50 Prakash Baskar – Founder and President

Prakash Baskar.jpg

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   


Prakash’s LinkedIn:


Khyanafi’s LinkedIn:

Set up a discovery call with Prakash:


  • “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! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

Fyrebox - Make Your Own Quiz!

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!

#49 Becoming a Kaggle Competition Master with Valeriy Babushkin – Head of Data Science, Kaggle Competition Master (Top 60)

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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


Valeriy’s LinkedIn:

Valeriy’s Kaggle:


  • “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! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

Fyrebox - Make Your Own Quiz!

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!

#48 Leverage What You Know to Get Your Foot in the Door with 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


Jay’s LinkedIn:



  • “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! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

Fyrebox - Make Your Own Quiz!

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!

#47 Transforming Government Organisations with Data Science with Marek Rucinski – Deputy Commissioner, Smarter Data Program

Marek Rucinski 1.jpg

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  


Marek’s LinkedIn:


  • “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.”

Now you can support Data Futurology on Patreon! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

Fyrebox - Make Your Own Quiz!

Marek Rucinski is based in Sydney, Australia.

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

#46 Today is the Best Time to be a Data Scientist with Jonny Bentwood – Global Head of Data & Analytics

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Jonny Bentwood – Global Head of Data & Analytics

Jonny Bentwood is the Global Head of Data & Analytics at Golin. Jonny is an innovative leader with 15+ years of experience in communications - winning, retaining and working for Fortune 100 clients such as Facebook, Unilever, Heineken, Barclays, HP and Microsoft. He has a proven record as a creator of pioneering solutions with ability to transform business to radically impact bottom line. Jonny presents complex information in an engaging and informative style and is a strategic consultant to executives using data to provide guidance on reputational and crisis issues and maximising marketing campaigns.

In this episode, Jonny tells a story about how MTV got in touch with him to apply data in figuring out who would most likely win The Apprentice. After being in the industry for over twenty years, he believes this is the best time to be in data. CMOS are spending more of their money than ever before on analytics. How do data scientist prove their value? People use data purely in a descriptive way. To succeed and bring value to clients, one needs to switch from describing the data to telling the customer what they need to do with the data. Set the goals of who, what, and why to figure out which message will be most useful before you even start. Take it a step further by using prescriptive data and make it predictive. This is where you study what will happen in the future. We are continually absorbing and understanding what things could happen and will happen. This opportunity is essential to identify issues before they occur and fix them.

Later, Jonny explains how understanding the customer requires a customer journey approach to increase marketing efficacy. Instead of doing random stuff, focus objectives with specific tactics and strategies. Something that gets on Jonny’s nerves is when people say it isn’t rocket science. Jonny wants people who do the research and figure out the information that counts. Then, we learn why organizations need to be data-driven. It is essential to train people and give them the technology to improve their jobs and become more efficient. Jonny challenges the status quo in his business. For instance, they have unlimited holiday, and their gender pay gap is positive to women.

Enjoy the show!

We speak about:

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

  • [04:50] Public relations

  • [06:00] Descriptive, prescriptive, and predictive

  • [08:15] Difference between interesting and useful

  • [10:00] Understanding the customer

  • [15:25] Cultural shift of data in organizations

  • [19:10] Challenging the status quo  

  • [22:40] Shiny object syndrome

  • [26:45] The twenty percent time

  • [30:00] Bringing data application to the masses

  • [34:30] Each stage of the customer journey  

  • [39:30] Getting value for money

  • [42:45] Return on investment

  • [44:15] Data + creativity  


Jonny’s LinkedIn:

Jonny’s Twitter


  • “To be truly smart you need to go from descriptive to prescriptive.”

  • “For a data scientist, the word interesting is one of the worst insults you can get. It has to be useful, what is the point you are trying to make?”

  • “There’s always going to be something else. What you need to do is focus on what you have.”

  • “Some of the best stuff has data infused with creativity.”

Now you can support Data Futurology on Patreon! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

Fyrebox - Make Your Own Quiz!

Jonny Bentwood 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!