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.
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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. 
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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.