Data Science Leaders

Wouldn’t it be great if there was a commonly agreed-upon framework for executing all AI projects successfully? Well, there isn’t one. However, there is CRISP-DM, the antediluvian “Cross-Industry Standard Process for Data Mining”, but you need to expand, modernize and adapt this framework for success at your organization.

In this episode from the archive, Dave Cole interviews David Von Dollen, former Head of AI at Volkswagen of America, about how they integrated CRISP-DM into an Agile process to drive more rapid iteration and, ultimately, more successful AI projects.

2023 has been an exciting year for AI, but it’s nothing in comparison to what we will see in 2024! Expect to see sensational successes amid the debris of projects that were set up for failure, a flowering of predictive AI, and the emergence of the scariest thing in AI to date (EU regulation). Tune in to this episode where Dr. Kjell Carlsson shares his top predictions for AI in 2024 and get ready for a year of scandal, fraud, plummeting processor prices, and ascendant AI leaders. Also, goodbye quantum computing!

Happy holidays from all of us at Domino Data Lab and the Data Science Leaders podcast.

What does it take to turn the latest advances in AI into products that deliver business impact at Walmart levels of global scale?
Srujana Kaddevarmuth is the Senior Director of Data & Machine Learning Programs at Walmart Global Tech. Her team drives data strategy and grapples with data science productization every day. With millions of employees, hundreds of millions of customers, and petabytes of data at any given moment, Walmart offers some unique lessons in the complexities of building teams, processes, and products to effectively leverage AI at scale.
In this episode, Srujana shares a few of those lessons, along with her perspective on nonlinear career paths, organizational collaboration and alignment, and her ongoing fascination with what’s next. Plus, she dives into her passion for fostering diversity in data science and tech, sharing strategies leaders can implement to help bring more women into the field.
We discuss:
What to prioritize when experimenting with next-gen tech
How to use “communities of practice” to align your organization
Solving governance, reproducibility, and knowledge sharing challenges at scale
Bringing more women into data science 


In this season finale episode, host Dave Cole also shares his three biggest takeaways from his many in-depth conversations with leaders in data science.
Stay tuned for a whole new season of Data Science Leaders coming soon! We're just getting started.
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

There’s tremendous value in pure data science research. In an enterprise context, however, it all comes down to how learnings and insights from that research can help advance business growth, customer experience, and product innovation.
Sunil Kumar Vuppala is the Director of the Global Artificial Intelligence Accelerator at Ericsson. His career journey from a researcher role to data science leadership has given him years of perspective on how ML professionals and their business side counterparts can build partnerships that pay off in both the near and long term.
In this episode, Sunil shares some of those key lessons on education, communication, and collaboration. Plus, he details a unique MLOps strategy he’s employed to address challenges with scaling model monitoring.
We discuss:
How a research background can inform leadership style
MLOps best practices for scale
Forming mutually beneficial partnerships between business stakeholders and data science teams



Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Large enterprises will always have some internal groups that are more change-averse than others. But progress often necessitates change, and how well you navigate the change management process can make or break your success as a leader.
Michal Levitzky is the Head of Data & Analytics (CDO) at Migdal Group, a leading insurance and finance company in Israel. Michal has spearheaded the introduction of data and analytics functions at multiple organizations, and she knows a thing or two about negotiating the complexities of change management during analytics transformations.
In this episode, Michal shares her advice for AI leaders driving meaningful change at their own companies. Plus she details her philosophy on structuring data and analytics teams for maximum efficiency and collaboration.
We discuss:
Using experience in fields like accounting as building blocks for leadership in data science
Change management during model-driven transformations
A structure to enable BI and data science functions to better support each other 


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Without a clearly defined methodology, complex projects with multiple technical and business stakeholders often fall apart. The risk is especially high when trying to scale data science work in an enterprise organization. 
That’s why David Von Dollen, Head of AI at Volkswagen of America, integrated agile methodology with CRISP-DM to help his team navigate roadblocks and accelerate progress on the path to model deployment. He shares how this hybrid approach enables his team to be more strategic about project lifecycles, unlocking real business impact even faster. 
Plus, David provides advice for building relationships with key business stakeholders and shares his philosophy on using the art of data science to benefit humanity. 
We discuss:
Implementing hybrid CRISP-DM and agile methodologies
Building relationships with stakeholders across the business
Using data science to solve challenges outside of work      


Mentioned during the show:
DataKind     


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. 
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Even with the recent rise of specialized data science degree programs, top-notch data science talent can come from anywhere. 
Those in leadership positions have a duty to share their knowledge and support aspiring data scientists, regardless of the unique path that brought them to the field. 
Sidney Madison Prescott, Global Head of Intelligent Automation (RPA, AI, ML) at Spotify, has made a habit of sharing her expertise and giving back. And in the process, she’s built a personal brand that would inspire future leaders in any industry. 
In this episode, Sidney shared her career story, offered advice for building diverse data science teams, and detailed her work in robotic process automation at Spotify. 
We discuss:
Sidney’s career journey and her guidance for women and people of color in data science
How a strong personal brand can open doors to opportunities in tech
Why data science leaders should care about robotic process automation  


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. 
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Model governance is vital, especially in heavily regulated industries like insurance.
Strong governance can help ensure that key models are reproducible, explainable, and auditable—all important factors for both internal model development workflows and for external regulatory compliance. But the best governance strategy isn’t always obvious.
Anju Gupta, VP Data Science & Analytics at Northwestern Mutual, is a big believer in establishing model governance practices early, and she shares her thoughts on the topic in the episode. Plus, she talks about some surprising roles on her data science team and the unique value that comes from pairing actuaries with data scientists.
We discuss:
How to establish scalable model governance practices
The intersection of actuarial work and machine learning
Roles you didn’t know you needed on your data science team


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

In every industry, people consume data. They work to understand what it can tell them in order to make smarter decisions.
But the nature of data in the world of life sciences presents some unique challenges—and opportunities—for data science.
In this episode, Sidd Bhattacharya, Director of Healthcare Analytics & AI at PwC, dives deep into these dynamics and shares his perspective on how leaders can operationalize AI at life sciences companies.
Plus, we talk about the role data science has played in the fight against COVID-19 and the remarkable effort to develop such highly effective vaccines.
We discuss:
How data science in life sciences compares to other industries
Operationalizing AI and measuring the ROI
Strategic recommendations for data science leaders
AI’s contribution to the fight against COVID-19


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Many people assume that once you establish a manufacturing line, the hard work is done and things remain relatively static. The reality, especially in electronics manufacturing, is entirely different.
Constantly changing data streams and endlessly dynamic variables present some unique challenges for data scientists in the field. But there are lessons on data sharing, model adoption, and real-time impact that ML professionals in any field can learn from.
In this episode, Alon Malki, Senior Director of Data Science at NI (National Instruments), opens a window into the world of data science in electronics manufacturing. Plus, he shares why human-in-the-loop processes are essential to gaining buy-in for AI in the enterprise.
We discuss:
Data science in electronics manufacturing
Strategies for sharing data to improve manufacturing processes
Human-in-the-loop applications
Looking for challenge-motivated data science talent  


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

When your data science team is consistently more reactive than proactive in addressing business challenges, it can be difficult to be seen as strategic partners.
But by prioritizing building business domain expertise and always asking about the “why” behind any request, you’ll start to build a rapport and change the nature of the relationship.
In this episode, Indy Mondal, Senior Director of Data Science, AI & Product Insights at DocuSign, explains how to create strong business partnerships to earn data science a critical and strategic seat at the table.
Plus, he shares his unique perspective on the business impact of models and why self-service tools are essential to delivering value.
We discuss:
How to use data science to inform business strategy
Using models to drive efficiency across the organization
The role of self-serve tools in data science 


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

To create sustainable business value, data scientists need to navigate all the elements of what this episode’s guest has dubbed “the data science and analytics value chain.”
So what are those elements? And how can you ensure you hire and develop the team that delivers on each one with every single data science project?
Nancy Hersh, Chief Data Officer at Arcadia, joins the show to break it all down.
We discuss:
Five elements of the data science and analytics value chain
How an apprenticeship model can bring data scientists closer to the business
Unique hiring strategies in an ultra-competitive market


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

The coding, models, and experiments inherent in data science work may have more to do with understanding human well-being than you think.
Machine learning and AI can be applied in ways big and small to further our understanding of human behavior—and influence our well-being.
Takuya Kitagawa, Chief Data Officer & Managing Executive Officer at Rakuten Group, believes there must be a shift toward focusing on well-being when it comes to how brands relate to customers. He joins the show to share his perspective on the future of data science, plus he details his approach to managing a large team spanning many products, cultures, and geographies.
In this episode, we discuss:
The role of ML in unifying the customer experience across multiple products
Managing globally distributed data science teams
Understanding human intention and well-being with technology


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Bias is an ever-present enemy of sound data science in healthcare.
Without proactive measures to mitigate bias in the data used to build and train models, real people can bear the brunt of potentially life-altering negative consequences.
Vikram Bandugula, Senior Director of Data Science at Anthem, knows this issue intimately from his extensive experience in healthcare. He joins the show to share his perspective on bias, plus he details his approach to fostering employee motivation and positive team morale.
In this episode, we discuss:
Problem-solving in data science and healthcare
Managing bias in healthcare data sets and models
Motivating high-performing employees and teams 


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Is your team mining all available data to inform your business strategy and grow revenue? Is your company prepared to compete against others who are?
If you’re like most, the answer is probably no.
How can you future-proof your organization and take steps toward an autonomous enterprise?
Janet George is an enterprise AI leader and author with experience across companies including Oracle, Apple, Accenture, Yahoo!, eBay, and more. She joins the show to discuss the meaning of autonomous enterprise and the process required for true transformation.
We discuss:
What is an autonomous enterprise?
Where are companies falling short in their data transformation?
The investment and first steps required on the transformation journey
How to prioritize data projects for a larger impact on revenue


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Who uses the models that we create and how do they use them? Those key questions underpin the notion of responsible AI. 
Since algorithms can have a significant societal impact, it’s vital that data scientists are aware of the broader context in which they may be applied. 
In this episode, Anand Rao, Global Artificial Intelligence Lead at PwC, breaks down why responsible AI should be an important consideration for every data science team. Plus, he explains what you need to be successful in AI consulting, and why a portfolio approach to ROI is the best way to demonstrate value to the business. 
We discuss:
The difference between AI in the 1980s and today
Why data science leaders should care about responsible AI
The ingredients for an effective data science consulting practice
ROI analysis in data science 


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. 
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

When highly disruptive events like the COVID-19 pandemic occur, data science teams may have to throw historical data out the window. Models trained on what happened in the past simply don’t work in a radically different present.
In this episode, Karin Chu, VP Data Science and Digital Analytics at Peapod Digital Labs, discusses how her team is tackling that challenge head on, particularly as the global supply chain crisis impacts sectors from grocery to apparel.
Plus, she explains why two things are so vital to the success of a data science team: ML engineers and a culture of communication.
We discuss:
How data science teams are navigating the supply chain crisis
The vital role of an ML engineer
Tips for communicating about data science in business


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Legal work may not be an obvious application of data science to many advanced analytics leaders. But that should change.
In this episode, Peter Geovanes, Head of Data Strategy, AI & Analytics at Winston & Strawn, breaks down the nuts and bolts of legal analytics and how it’s revolutionizing the way law firms win new business—and cases. Plus, he shares insight on the types of legal challenges data science can help address inside any organization.
We discuss:
The role of advanced analytics in the legal sphere
Use cases on both the business and practice sides of law
How analytics leaders and general counsels can work together
What’s next in the world of legal analytics  


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Managing a large enterprise team of data scientists can be a complicated undertaking. There are so many opportunities, big and small, to serve the business with AI and machine learning. How do you ensure your teams are focused on the big picture without getting bogged down in the minutiae of the day to day?
Jan Neumann, Executive Director, Machine Learning at Comcast, leads a team of about 300 data scientists, divided into eight different focus areas. If anyone knows how to manage a large data science team, it’s him.
In this episode, he shares his strategies for effectively managing a team of this scale in the enterprise. Plus, he explains why he prioritizes continued learning, and shares tips for building out a feature store.
We discuss:
- Managing large data science teams at scale
- Making time to gain knowledge from the ML community
- What a feature store is and why data scientists should care
Mentioned during the podcast:
- The Idealcast with Gene Kim
- Mik + One with Mik Kersten
- a16z Podcast
- Yannic Kilcher on YouTube
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

As compute capability continues to expand, the banking industry is turning more and more to data science to enable better customer experiences.
Use cases have proliferated, from product recommendation engines to predictive customer retention alerts. These innovations can drive real business value, but managing the rollout of process and technology changes always presents interesting challenges.
In this episode, Chun Schiros, SVP, Head of Enterprise Data Science Group at Regions Bank, reveals how her team is leveraging AI solutions to optimize the banking experience. And with insight applicable to data science leaders in any industry, she shares her change management tips for driving adoption of machine learning among data skeptics.
We discuss:
- How data science use cases have evolved in the banking industry
- AI solutions in banking that optimize the customer experience
- Change management tips for winning over data science skeptics
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Should your team patent its data science work? With open source such an important part of the data science community, patents almost seem antithetical to the ethos of the field itself.
But it turns out, there are some very good reasons to pursue data science patents in business.
In this episode, Kli Pappas, Associate Director of Global Analytics at Colgate-Palmolive, shares his team's process for deciding whether to patent an algorithmic process—and what benefits it can bring. Plus, he talks about why a statistical background is so important for teams that generate data.
We discuss:
- The transition from getting a PhD in chemistry to the analytics world
- Finding the balance between statistical and computer science backgrounds
- Why you should patent your data science work and how to do it
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

There are many ways to structure a data science function in a global enterprise. But what’s been the winning strategy for global technology distributor Ingram Micro? Creating a data science “nerve center.”
Centralizing data science talent has helped elevate analytics at Ingram Micro to better solve complex business problems using machine learning and AI.
In this episode, Tim Suhling, VP Global Business Intelligence at Ingram Micro, explains how it all happened, and what data science leaders everywhere can learn from the transformation. Plus, he shares his perspective on how data science can impact “Customer 360” programs and different approaches to measuring the success of models.
We discuss:
- The relationship between data science and business intelligence
- Embarking on a customer 360 initiative
- Measuring the effectiveness of data science
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

To embed models into SaaS platforms at scale, it pays to have a cross-functional team—software engineers, UX designers, data scientists, machine learning engineers—all working together.
That collaboration allows you to tackle hard challenges around scaling models to work across hundreds of thousands of customers. And it enables you to build something that offers tremendous value across many different use cases.
Jayesh Govindarajan, SVP Data Science & Engineering at Salesforce, joins the show to share how his team makes this a reality. Plus, he talks about the priceless value of customer feedback and the three areas where data science teams should focus their efforts.
We discuss:
- Arriving at data science from a pure engineering background
- Why telemetry is no substitute for customer feedback
- Tips for embedding models into a SaaS product
- The three pillars of work for a data science team
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

In healthcare, only 14% of scientific discoveries actually make it into clinical practice. But data science, in lockstep with the digital transformation, is helping to change that.
As healthcare data and clinical studies transition to digital form, the opportunity to use data science and AI to generate insights and recommend treatment pathways is greater than ever. And the ability to make healthcare delivery more equitable is within reach.
In this episode, Kaushik Raha, VP Data Science & Health Content Operations at Elsevier, explains how data science is transforming the healthcare industry. Plus, he shares his thoughts on bias and some best practices for operationalizing data science.
We discuss:
How data science is helping to modernize healthcare
Working with clinical analytics to root out bias
Advice for operationalizing data science  


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

As business leaders become more educated on the value that machine learning can deliver, the demands on data science teams only become greater. Business stakeholders are now interested in much more than the accuracy of predictive models. They’re asking questions about productionization, scalability, and bottom line ROI.
In this episode, Nimit Jain, Head of Data Science at Novartis, joins the show to explain how this sea change is transforming how data scientists approach proof of value. Plus, he talks about how companies are adopting responsible AI practices and provides a window into the world of customer experience analytics.
We discuss:
- How proof of value has evolved over time
- The principles of responsible AI
- Customer experience analytics use cases
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 professionals at all levels can position themselves to win in a burgeoning market. Plus, he offers advice on how data science leaders can stimulate collaboration and intellectual curiosity within their organizations.

We discuss:
- How to stand out from your peers
- Intellectual curiosity, innovation, and collaboration in large organizations
- Being the CEO of the data science project you’re working on

Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.

Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 Hall, Director of Enterprise Analytics at McKesson, explains how the portfolio management process sets the groundwork for defining KPIs that track the actual value derived from predictive models and insights. Plus, she shares her thoughts on a process for validating model accuracy and managing risk.

We discuss:
- Tips for working with business counterparts
- Data science portfolio management
- Model risk management
- Supply chain analytics

Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.

Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 into the steps required to stand up a data science practice. Plus, he shared what inspired him to go back to school, and gave listeners a unique peek into the world of marketing analytics.

This episode features Mike’s insight on:

Starting data science practices from scratch
The complexities of marketing analytics
The value of continuous learning


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.

Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 types of analytics teams he’s seen work well during his career as a data science leader.
Topics covered include:
- If and how individuals can know what companies are doing with their data
- How GDPR and CCPA portend the future of data
- Structuring, growing, and managing different styles of analytics teams
Check out these resources mentioned during the show:
- Analytics: How to Win With Intelligence
- Building Analytics Teams
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 end of model implementation to answer the key question of “did it work?”
Robin shared her perspective on:
- Turning a PhD in public health into a career as a data science leader
- Navigating data science and clinical trials
- The life cycle of product analytics
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 , explains how his company approaches people analytics, and what all data science leaders can learn from the discipline. Plus, he offers a view into the hiring process Paylocity uses to add top-notch data science talent to its team.

The conversation covers:

People analytics and HR
Data science in the hiring process
Embedding data science into SaaS platforms


Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.

Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 leaders scaling teams of any size in the enterprise.

The conversation covers:
- Building an analytics team from the ground up
- Approaches to managing shadow IT
- Tradeoffs between distributed vs. delegated data science teams

Tune in on Apple Podcasts , Spotify , our website , or wherever you listen to podcasts.

Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 insights that can help save lives. Plus, she shares the unique journey that took her from economics and marketing to data science.

We discuss:
- Susan’s unique path to becoming a data science leader
- Sifting through messy healthcare data
- How to define measurable data science outcomes and gain buy-in

Tune in on Apple Podcasts , Spotify , our website, or wh erever you listen to podcasts.

Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 in business.

In this episode, Chris shares his thoughts on how computer science and statistics fundamentals can help us continue to push data science forward. Plus, he offers advice on how to conduct all-important exploratory data analysis (EDA) effectively.

We discuss:
- How computer scientists influenced data science
- What statistical thought brings to the equation
- Tips and tricks for doing EDA right

Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 you think.

In this episode, Eitan Anzenberg, Chief Data Scientist at Bill.com, offers his advice on getting started with deep learning. 

We discuss: 
-Testing, trusting, and understanding your data and your models 
-Advice for reducing bias in highly regulated industries 
-Considerations for getting started with deep learning 
-Challenges of deep learning as a discipline 

Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts. 
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 effectively.

In this episode, we discuss:
-Knowing your data and communicating it to the business
-Where to begin when launching a data science program
-International differences in data science

Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.

Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 shared his perspective on:
- How a background in risk analytics and consulting prepared him for a move into supply chain analytics
- The effectiveness of the citizen data scientist
- Who should lead a data science project

Tune in on Apple Podcasts , Spotify , our website , or wherever you listen to podcasts.

Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 efficiency and productivity for everyone at an enterprise scale.

Mark covered:
Why data science and data engineering should be under one roof
How data science helped keep Americans connected when COVID-19 drove massive shifts in internet usage
Different ways to approach embedding data science into production systems

Tune in on Apple Podcasts , Spotify , our website , or wherever you listen to podcasts.

Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 world-class data science teams in the process.
We also discuss:
- Making an impact with data science in the ads business at eBay
- Getting buy-in for data science experiments
- Hiring tips for leaders growing their organizations
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 leaders in the field.

The conversation covers:
- The past, present, and future of data science
- The different roles and responsibilities within a data science team
- New and exciting advancements in NLP
- When models are right for the wrong reasons

For daily news and insights on all things data science, follow @MikeTamir on Twitter.

Tune in on Apple Podcasts , Spotify , our website , or wherever you listen to podcasts.

Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 definition and foundational pillars of Industry 4.0
-Three approaches to Industry 4.0
-Balancing data science and domain expertise to deliver value
-Inspiring action through predictive analytics
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 discuss strategies for cultivating partnerships between data science leaders and the Chief Data Officer.
They also explored questions such as:

What is the role of a CDO?
What does “data as a product” mean?
How can the CDO enable others to deliver insights?
How do you start a data literacy program?
What’s the deal with “citizen data scientists?”

Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 Blue Cross and Blue Shield of Illinois, Montana, New Mexico, Oklahoma, and Texas, talks to us about the processes his team has put in place to overcome these unique challenges.

In this episode, we cover:
- The challenges of working in a highly-regulated industry
- Implementing a continuous code review process
- Where IT intersects with the data science life cycle

Tune in on Apple Podcasts , Spotify , our website , or wherever you listen to podcasts.
Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

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 cancer and the coronavirus
- The role of data science in the development of the COVID-19 vaccine

Check out these resources we mentioned during the podcast:
- COSMIC genetic database
- The 1000 Genomes Project
- gnomAD Genome Aggregation Database
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Listening on a desktop & can’t see the links? Just search for Data Science Leaders in your favorite podcast player.

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, Technology Fellow, and Chief Data Scientist at Halliburton, to discuss how to explain complex subjects in simple terms for better business outcomes.
Dr. Priyadarshy also explained:
- How his experience as a professor has influenced him as a leader in data science
- What governance he puts in place for his training methodology
- How to cultivate a diverse workforce
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Listening on a desktop & can’t see the links? Just search for Data Science Leaders in your favorite podcast player.

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 measure search success
- The difference between ML engineers and ML scientists
- How to design teams around product orientation
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Listening on a desktop & can’t see the links? Just search for Data Science Leaders in your favorite podcast player.

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 leaders and their business counterparts.
 
They also talked about:
- How to structure your organization for great partnerships
- Cultivating your teams’ soft skills alongside their technical skills
- How to measure stakeholder engagement and partnership success
- What to do with difficult partnerships
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.

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 measure AI reliability
- The difference between DevOps and MLOps at McAfee
- How adversarial machine learning works
- How to build out a more diverse data science team
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Listening on a desktop & can’t see the links? Just search for Data Science Leaders in your favorite podcast player.

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 data scientists.
What we talked about:
-Building a data science team at the New York Times
-Creating a data culture
-The relationship between a data science team and a data analyst team
-Necessary soft skills for data scientists
Some resources mentioned during the podcast:
hackNY
Beautiful Data: The Stories Behind Elegant Data Solutions
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Listening on a desktop & can’t see the links? Just search for Data Science Leaders in your favorite podcast player.

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!