Data Science Leaders

How do you implement an enterprise-grade GenAI application that serves millions of users a day? By focusing your application and building the capabilities for operationalizing it at scale.Join our upcoming fireside chat with Domino's SVP of Product, Chris Lauren, who will share lessons learned while operationalizing the world’s first enterprise-grade GenAI application to be used on a global scale, Github Copilot.Join us to learn:Success factors for GenAI use casesCommon challenges and how to avoid themKey capabilities for operationalizing GenAI models at scaleInferencing GenAI models cost-effectively

Every organization has an abundance of outlier detection use cases, but how do you turn them into repeatable, scalable AI products that drive a virtuous cycle of adoption and impact?To answer this question, Jan Zirnstein, Senior Data Science Director at Honeywell,. shares their best practices for successfully driving value using anomaly detection, how to build trust with stakeholders, and the importance of both product management and software development resources.Join us as we discuss:How to spark a virtuous cycle with anomaly detection use casesDriving continuous improvement by transitioning from unsupervised to supervised machine learningAligning the model development and software development lifecycles

AI has enormous potential for good, not least in helping us make more ethical, sustainable decisions as investors and consumers. In this week’s episode Ron Potok, Head of Data Science at Clarity AI, explains how AI helps us overcome the challenges of collecting, normalizing and assessing Environmental, Social and Governance (ESG) data and making that data useful and convenient to humans when making decisions. Indeed, he reveals how AI can bring transparency to human-only ESG ratings that can be more opaque and prone to bias than an AI model, and the benefits of leveraging humans and AI models in tandem.Join us as we discuss:Overcoming the ESG data quality challenges with AILeveraging AI to contextualize data and drive consistency How AI can provide greater transparency than human-only ratings

How do you trust black-box AI models with decisions that will make-or-break your business?This week we speak with Gregory Zuckerman -- special writer at the Wall Street Journal and author of The New York Times bestseller of The Man Who Solved the Market -- to find out how the pioneers in algorithmic trading learned to stop worrying and trust their AI systems. Join us as we discuss:How trust in AI relies on trust in people and processesThe limits of explainability and transparencyThe power of systems over stories

Who doesn’t have a data science talent gap? Anyone? Most organizations struggle to realize their AI ambitions because of a lack of data science skills, a disconnect between the technology and the business domain, and a lack of leadership experience with AI.Halliburton has been solving all three of these challenges with one of the earliest and largest corporate data science programs in the energy sector.In today’s episode of Data Science Leaders, we are extremely fortunate to be joined by Dr. Satyam Priyadarshy, Managing Director, Technology Fellow and Chief Data Scientist at Halliburton who shares their best practices for upskilling talent, bridging the data science - business divide, and ensuring executive engagement.Join us as we discuss:How to upskill existing domain experts on data science methodsHow to engage and drive alignment with corporate stakeholders through workshopsThe benefits of upskilling domain experts on code-based data science toolsThe importance of involving and upskilling leadership

Join Jen Stave and Catherine Feldman from Harvard's D^3 Institute as they discuss the challenges of AI-driven transformation, the need for strong leadership, and the importance of experimentation. They explore the reasons behind AI project failures, the holistic approach to AI success, and the application of generative AI. Don't miss their insights on disruption, building a digital-first culture, and the impact of the Digital Data and Design Institute.

Generative AI is here and, unless you’ve been cloistered in a cave, you already know it’s making waves in nearly every industry. But when it comes to this shiny new technology, separating fact from fiction can become quite a challenge.Luckily, in this episode, Rowan Curran, Analyst at Forrester, joins the show to demystify the latest leaps in AI tech, help you apply it to your business today, and give a glimpse of how it will affect the business landscape of tomorrow.Join us as we discuss:Separate generative AI facts and fictionTake a closer look at AI applications you can start using todayExamine the future of AI and its impacts on the workforce and the workplace

“It is on the shoulders of leaders that they build and maintain an ethical AI risk program.” That’s the message Reid Blackman – author of “Ethical Machines” and founder CEO at Virtue Consultants – shares in this episode. He discusses the real ethical AI concerns — blackbox models, bias, hallucinations, privacy violations and more — and explains the crucial need for leadership accountability, buy-in from the very top of the organization, and a multi-party effort in building and maintaining AI ethical risk programs.Join us as we discuss:Why AI poses greater ethical risks than other technologies (and humans)Leadership and the other key elements of a successful AI / digital ethics programThe importance of explainability

When it comes to driving business impact with AI, there are no silver bullets, but data science product management comes pretty close. It could well be the key to bridging the gap between business and technical teams, designing solutions to meet the business need, spurring ideas from experimentation to implementation, and driving continuous improvement. But how do you build a product management capability for data science?In this episode, Alek Liskov, Director of AI & Data Product Management at Verizon, shares their hard-won best practices in building data science product teams and their phenomenal successes in delivering AI-driven impact.Join us as we discuss:The emerging discipline of data science product managementHow to build durable product teams for data scienceWhere to find and how to develop data science product managers

What’s different about the AI wave today versus the 1980s and what do the latest advances reveal about our human intelligence? We’re behind the scenes at Rev4 with Steven Levy, best selling author and Editor at Large at WIRED. Steven shares insights he’s built over the past four decades writing about AI and the people (like Marvin Minskey) and companies (like Google and Facebook) that have brought us to where we are today. Join us as we discuss:A brief history of AI - from dashed hopes to triumphant transformersHow AI is helping us understand human intelligenceThe regulatory risks of anthropomorphizing AI