Sign In
Register

Partnerships

Tickets

Call to action
Your text goes here. Insert your content, thoughts, or information in this space.
Button

Back to speakers

Nitesh
Soni
Global AI Solutions Leader
Sanofi
Nitesh Soni is a distinguished leader in Data Science and AI, boasting over 18 years of diverse experience across the pharmaceutical, finance, consulting, and research industries. Since joining the Digital GBU team at Sanofi in 2022, Nitesh has been instrumental in developing and operationalizing high-priority, scalable AI products. He leverages data, expert AI, and Generative AI (GenAI) to create solutions that are not only innovative but also compliant and responsibly built. Throughout his illustrious career, Nitesh has excelled in strategic, execution, and operational roles, driving digital transformation within large, complex global organizations. He is renowned for his ability to build and lead diverse, high-performing global data science teams. His passion for coaching and mentoring young talent in data science and AI is evident in his dedication to nurturing the next generation of leaders in the field. A highlight of Nitesh's research career includes being part of the groundbreaking team that discovered the Higgs boson particle in 2012, a milestone that underscores his commitment to advancing scientific knowledge and innovation.
Button
20 November 2024 16:00 - 16:30
Panel | Building in regulated industries: Overcoming challenges in risk, regulation & bias
Deploying GenAI in regulated industries pushes engineering teams to balance innovation with control. From model explainability to bias mitigation and auditability, this session explores what it takes to build compliant systems that still deliver value. Learn how teams are navigating legal constraints, cross-functional oversight, and evolving standards without grinding progress to a halt. Key takeaways: → What needs to shift in your architecture when building for auditability and traceability. → Why fairness and compliance aren't just policy issues, they're system design problems. → How to ship GenAI in high-stakes environments without getting buried in red tape.