Associate Director Product Management - Enterprise AI

Associate Director Product Management
- Enterprise AI  Enterprise Data & AI | Tarrytown, NY ROLE SUMMARY Regeneron’s newly established Enterprise Data & AI organization is built to drive AI transformation and adoption at scale across every part of the enterprise. This is a high-visibility, high-velocity position where the work is directly tied to business and scientific impact. As a product leader for Regeneron's AI platforms and tools, the successful candidate will have the potential for massive impact and help shape how the enterprise builds, accesses, and adopts AI capabilities.

The Associate Director Product Management
- Enterprise AI will own the roadmap for a defined portfolio of enterprise platforms and tools that put AI into the hands of colleagues across the organization, from internal AI assistants to secure data-connected applications to agentic solutions. They will translate business needs into clear technical requirements, frame decisions with a transparent view into their assumptions and the rigor behind them and proactively use data to inspire the next generation of capabilities.

The role also applies a forward deployed engineering approach, embedding with global development and business unit teams to deploy AI and agentic solutions into production and returning field signal to shape the platform roadmap. The successful candidate will be an integral member of the Enterprise Data & AI (EDAI) team, and will work closely with the Chief AI Officer’s leadership team, the AI Innovation leader, the Platform Engineering team, Delivery Managers, and Business Unit IT partners.

Together the team will leverage Regeneron's rich data and state-of-the-art infrastructure to deliver platforms that are useful, trusted, and adopted at enterprise scale. As Associate Director Product Management
- Enterprise AI, a typical day might include the following:
Product Strategy & Roadmap Own the release strategy and roadmap for an assigned portfolio of Regeneron's AI platforms and tools, aligned to the enterprise digital roadmap and the EDAI platform strategy Build business cases to develop, acquire, or invest in new AI/ML capabilities, datasets, platforms, and tools Prioritize ruthlessly across competing demands, framing recommendations with clear assumptions and analytical rigor.

Develop deep insight into colleague and end-user pain points and identify the highest-value opportunities to improve their experience. Platform & Tools Delivery Lead the technical product development of assigned components of large-scale AI platforms, machine learning/AI models, and supporting tooling, partnering with engineering and Business Digital teams to ship high-quality deliverables. Define requirements for core platform capabilities: data access, model serving, search/ranking, automation pipelines, and self-serve tools.

Guide the use of cloud technologies (AWS, Azure, GCP) for building and deploying automated ML and analytics pipelines. Run evaluation, pilot, and deployment of new technologies against the framework and criteria set with product leadership, building toward an industry-leading internal AI ecosystem. Drive product adoption: measure usage, gather feedback, and continuously improve. Partner with GCC engineering teams to translate platform architecture decisions and product requirements into well-scoped, production-ready deliverables;

maintain ongoing alignment on roadmap priorities, handoff standards, and delivery quality to ensure continuity between applied AI prototyping and GCC-led build and operate. Forward Deployed Engineering Embed with business and scientific teams to scope, build, and deploy AI and agentic solutions from first prototype to stable production. Own discovery, technical scoping, system design, and production rollout for priority deployments, working alongside BU IT and Platform Engineering.

Contribute hands-on to prototypes, integrations, and evaluations when progress or clarity depends on it. Evaluate and onboard new AI model capabilities, including model upgrades, tool use, and MCP-based integrations, against quality, safety, and cost criteria. Capture field feedback on where models and platforms succeed or fall short, and feed it into the roadmap and evaluation suites. Codify repeatable patterns into reusable building blocks, playbooks, and reference architectures for adoption across business units.

This role might be for you if:
Bachelor's degree in related field required, Masters, MBA, or advanced degree…