Data Science Salon Podcast Dat Science Salon

In this episode, Anna sits down with two leaders in the finance industry, exploring the forefront of AI and ML innovations.

First, we have Mabu Manaileng, Lead Data Scientist at Standard Bank Group. Mabu shares his journey and current role, highlights the challenges of applied data science in the financial sector, and discusses the transformative impact of AI on banking in the coming years.

Next, we welcome Adam Lieberman, Head of AI and ML at Finastra. Adam defines the concept of drift, discusses statistical measures to quantify it, and provides strategies for maintaining model health, ensuring that models continue to serve users' needs effectively.

In this episode, Anna sits down with two leaders in the finance industry, exploring the forefront of AI, ML, and ESG innovations.

First, let's welcome Laura Gabrysiak, Data Science Leader at Visa. Laura develops statistical models and decision analytics tools that enable Visa clients to transform massive amounts of data into actionable ML models and AI implementations. She's also passionate about fostering the local data science community in Miami as the Founder of R-Ladies Miami. In this conversation, they dive into the future of ML/AI in financial services and the impactful work being done with Code Art to promote diversity in tech.

Next, we have Rochelle March, former Head of ESG Product at Dun & Bradstreet. Rochelle specializes in impact analysis related to carbon, water, and the Sustainable Development Goals, and applies machine learning to ESG products. She also teaches data and analytics at Bard College’s MBA program, sits on the advisory board for USL Technology, Inc., and mentors fellows in the Environmental Defense Fund’s Climate Corps program. Since recording this episode, Rochelle has started her own company, People Places Words Actions. In our discussion, we explore her journey in ESG innovation and analytics, why ESG data is crucial for responsible investment decisions, and how it drives sustainable business practices.

Tune in to learn from these industry thought leaders and gain insights into the cutting-edge applications of AI and ESG data in the finance sector.

In this episode, Anna sits down with two distinguished leaders in the ML/AI finance industry. First, we have Harry Mendell, Technology Group Data Architect at the Federal Reserve Bank of New York, who brings over 30 years of expertise in FinTech. Harry shares compelling stories and discusses emerging trends in the finance sector.
Following Harry, Supreet Kaur, AVP at Morgan Stanley and product owner for various AI products, joins the conversation. Supreet provides insights into the use of synthetic data to protect customer privacy in FinTech, ensuring informed decision-making. This deep dive into synthetic data highlights its growing importance in the industry.

Ben Dubow of Omelas joins us to talk about data in context, NLP/NER at scale, and the impact of generative AI on democracy + authoritarianism.

Data scientist-turned-product person Noelle Saldana has experienced the "sprinkle some AI on it" request more times than she'd care to remember. Our Senior Content Advisor Q McCallum met up with Noelle to explore this phenomenon. How does this happen? (Hint: "corporate FOMO.") What should you do when stakeholders insist on implementing AI that isn't actually going to help? What about when your data scientist peers seem like they're doing this for the sake of "résumé-driven development?"

Sometimes the most valuable data IN your company ... is the data LEAVING your company.
That's Solomon Kahn's view on data products, as well as the premise behind his latest venture: Delivery Layer.
For this episode, our Senior Content Advisor Q McCallum reached out to Solomon to check in on the new startup, and to tap his expertise in the world of data products.
Solomon's been at this a while. He's run high-revenue data products in some notable places, including Nielsen. Over the years he's learned a lot and we're excited for him to share some of that hard-earned knowledge here on the show.
In this extended conversation, the two explore: the reasons why building a data product is different (and, in many ways, more difficult) than building traditional software products; how the people involved can impact the outcome; why a good sense of risk management can make all the difference; and what purple cars have to do with all of this. (No, seriously. Purple cars.)
Along the way, the pair talk about the early days of the data field, and how much it has changed.