Data Science Leaders

Talent is pouring into data science, even though it always seems like there’s not enough to meet demand. Learning opportunities for people getting into the field have exploded in just the past decade.  That means standing out from the crowd—both as a leader and as a practitioner—has become more important than ever before. In this episode, Bob Bress, Head of Data Science at FreeWheel, explains how...

You may not have a formal “portfolio management” function within your data science team, but in all likelihood, you’re executing some of its key components already.  But being more intentional around portfolio management can pay big dividends. Without it, you could be missing out on a powerful and holistic way of demonstrating the value your team provides to the business. In this episode, Katya Ha...

Mike Foley has been building data science teams from scratch since before they were called “data science” teams. His perspective on questions like “Where do I start?” or “How do I get buy-in?” can help leaders growing data science teams of any size avoid some pitfalls along the way. Currently the Senior Director of Data Science at Hitachi Vantara, Mike joined Dave for a conversation that goes deep...

Between GDPR, CCPA, and more regulatory frameworks on the horizon, the landscape of personal data—and how it can be used in business—is shifting. On this episode, John Thompson, Global Head of Advanced Analytics & AI at CSL Behring, joins host Dave Cole to discuss that shift, and a potential future in which we as individuals could be compensated for the use of our data. Plus, John shares the two t...

In a perfect world, healthcare data would always be strategically organized, up-to-date, and easily accessible—all in a patient-centered, privacy-first way. But the reality is much more complex. Robin Foreman, Director of Data Science at CVS Health, joins the show to discuss the challenging world of data science in clinical trials. She also explains how product analytics can be used on the back en...

People analytics—the application of data science and analytics in the world of HR—can provide valuable insights into recruitment, retention, and productivity. But when working with people's sensitive demographic, compensation, and performance data, ethical and privacy considerations must come first. In this episode, Adam McElhinney, Chief Data Sc ience Officer, VP of Data Insights at Paylocity , e...

Dave Frankenfield , VP Enterprise Data and Analytics at Optum , oversees a team of 2,700 data professionals. How do you structure a team of that size? What functions does it cover? And how does it collaborate with and deliver value to the rest of the company? In this episode, Dave discusses the strategies he’s used to build his team, the lessons he’s learned, and the advice he has for data science...

Data plays a vital role in cancer treatment. In oncology analytics, data analytics can help identify promising treatment strategies, offer better access to affordable care options, and provide critical feedback to medical teams. In this episode, Susan Hoang , Vice President of Oncology Analytics at McKesson , shares how her team overcomes the inherent challenges of messy healthcare data to deliver...

Computer scientists can be fearless, pushing the limits of computational power and the scale of data we can analyze. On the flipside, statisticians can be intensely skeptical, always measuring error and bringing a critical perspective. According to Chris Volinsky, AVP - Data Science & AI Research at AT&T, it’s these two schools of thought that combine to make data science such a powerful function...

Forward-thinking companies are already embedding machine learning into their business processes—and seeing the payoff of model-driven decisioning. But what about deep learning? How can ambitious data scientists get started with deep learning? How can they satisfy their own curiosity, and eventually apply new approaches to address real business challenges? The field may be more approachable than yo...

As a data scientist, you must be able to explain complex ideas in simple ways. Knowing your data, knowing the business, and presenting the data clearly to business stakeholders is an essential part of the role. Gaia Bellone, SVP - Head of Data Science at KeyBank, has a passion for leading and training her data science team. Her priority: ensuring that her team is successful at communicating data e...

Data science jobs outnumber data scientists by three to one. The industry is looking for ways to close that gap, including turning to the concept of the citizen data scientist. But in today’s episode, Romain Ramora , Head of Data Science & Innovation - Supply Chain at Cisco , shares why he thinks we shouldn’t be putting critical models in the hands of people lacking the proper expertise. Romain sh...

It’s a common refrain among enterprise data science professionals: 70-80% of their time is spent on data wrangling and pipeline building. But what happens if you bring data science and data engineering together under one roof? Mark Teflian , VP, Data Science and Data Engineering at Charter Communications (Spectrum), joins the show to share how bringing the functions together can help increase effi...

In data science, experimentation is everything. But as a leader, how can you balance experimental work that may never pay off with delivering measurable business value every day? In this episode, Khatereh Khodavirdi, Global Head of Analytics & Data Science - Global Merchants at PayPal, talks with host Dave Cole about how she has navigated that balance throughout her career, all while building worl...

The title of “Data Scientist” leapt into prominence in 2012 when the Harvard Business Review named it the “sexiest job of the 21st century.” Almost ten years later, what’s changed? And what’s next? In this episode, Dave Cole is joined by Mike Tamir , Chief ML Scientist and Head of Machine Learning/AI at SIG , to break down the shifting trends in data science, NLP, and ML—and what it all means for...

We’re in the middle of the fourth industrial revolution. Industry 4.0 encompasses the use of advanced automation and analytics in manufacturing. So how is data science driving value in Industry 4.0? In this episode, Dave Cole is joined by Paul Turner, Vice President Industry 4.0 Applications & Analytics at Stanley Black & Decker, to break down everything you need to know. We discuss: -The definiti...

As more organizations recognize the power of data to transform their decision making (or for it to become a product in its own right), the role of the Chief Data Officer has become critical. So what are the biggest challenges facing every good CDO? And where do data science teams intersect with that work? In this episode, Dave Cole is joined by Heidi Lanford, Chief Data Officer at Fitch Group, to...

Data scientists in the healthcare industry face some especially tough challenges. Not only do they have to contend with complex regulatory landscapes impacting the data they can work with, but they’re also constrained by some less than modern processes. 75% of medical communication is still delivered by fax. And that’s just one example. Derrick Higgins , Head of Enterprise Data Science & AI at Blu...

The field of bioinformatics plays a critical role in medical breakthroughs like the COVID-19 vaccine. Fiona Hyland , Director of R&D, DNA Sequencing Informatics at Thermo Fisher Scientific , teaches all of us about how it happened in the latest episode of Data Science Leaders. What we talked about: - A quick run through genetics and bioinformatics terminology - Bioinformatics, and the genetics of...

The best data scientists are continually learning something new, taking on unfamiliar projects, and keeping their skills fresh. The best leaders in the industry create a culture where teams have opportunities to grow and are able to clearly understand and communicate data science concepts. In this episode, Dave Cole is joined by Dr. Satyam Priyadarshy, Managing Director for India Center, Technolog...

Data science operationalization is a simple enough concept. But in practice it can be a complicated and often overwhelming challenge. In this episode, Dave Cole is joined by Nishan Subedi, VP, Algorithms at Overstock.com, to discuss the best way to operationalize data science. Nishan talked about: - The data science experts that make up the team at Overstock - Strategies to improve search and meas...

Data science teams are responsible for delivering impactful models, of course. But they’re also responsible for translating that impact (and all the work that goes into it) for business stakeholders of all kinds.   In this episode, Dave Cole is joined by Nate Litton, Vice President, Data & Analytics at Toyota North America, to discuss strategies to strengthen the relationship between data science...

What if we could predict how long our models will last in the field? Is there a mathematical way to estimate mean time to failure for a specific model? In this episode, Dave Cole is joined by Celeste Fralick, Chief Data Scientist at McAfee, to discuss AI reliability and how it can help predict model decay. Celeste also explained: - What AI reliability measures - Processes to put in place to measur...

This is Data Science Leaders from Domino Data Lab, with me, Kjell Carlsson. On this show, you’ll have a front-row seat to conversations featuring world-changing data science leaders. Join us for transformative stories of real people using machine learning to harness the power of data science to tackle the world’s most important challenges. Get it wherever you get your podcasts!

Algorithms can have an outsized impact on society. That’s why many data science leaders have focused a lot of effort recently on defining data literacy and ethics in a way that’s operationalizable in their company culture. In this episode, Dave Cole speaks with Chris Wiggins, Chief Data Scientist for the New York Times, about why a foundation of data literacy and data ethics is so important for da...