Data Science Leaders

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 cycleTune 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 vaccineCheck out these resources we mentioned during the podcast:- COSMIC genetic database - The 1000 Genomes Project - gnomAD Genome Aggregation Database 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.

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 workforceTune 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 orientationTune 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 partnershipsTune 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 teamTune 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!

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 scientistsSome resources mentioned during the podcast:hackNYBeautiful Data: The Stories Behind Elegant Data SolutionsTune 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.