Executive Director, Data, Machine Learning & AI

What is the Henry Schein ONE Way? Simply put, we care for each other. We treat each other with respect, kindness, gratitude, and awe. We welcome different viewpoints and encourage creativity. Henry Schein ONE believes that everyone has something amazing and unique to contribute, and we wouldn’t be Global Industry leaders today without all the individual contributions that bring our team together.

Our culture strives to provide a place where passion, individuality, autonomy, purpose and diversity succeeds. We strive to let you Schein because when you Schein so do we!

If you are still not sold on how great it is to be a Team Schein Member, then perhaps you need to hear about our Henry Schein Cares programs, team engagements, lunches, and extra wellness benefits. Or that our leadership encourages you to maintain a healthy work-life balance. There are so many perks too numerous to list. If you are intrigued, apply now, our Talent Acquisition team is excited to meet you!

This role is responsible for reliably delivering the company’s initiatives in Big Data, Data Science, Machine Learning, and the safe, effective use of LLMs. Success requires close collaboration across teams, along with clear, kind, and consistent communication. The role will offer frequent opportunities to mentor and develop others. As the company embarks on a significant, multi-year, cross-disciplinary data investment, fast and dependable execution on these high-priority initiatives is essential.

What You Will Do

• Set enterprise data & AI strategy in coordination with Leadership that aligns to company and product strategies; define a 2–3 year roadmap for data platforms, analytics, ML/AI capabilities (including GenAI), and business value realization.

• Deliver on the data platform (data lake/lakehouse, warehouses, streaming) evolution; ensure scalability, reliability, cost efficiency, and performance across our cloud environments. Architecture will be owned by the Architecture team, so a close working relationship is necessary.

• Establish and chair data & AI governance (policies, standards, data contracts, model governance), balancing innovation with risk, privacy, and regulatory compliance (e.g., HIPAA, GDPR/CCPA)

• Operationalize MLOps/LLMOps: implement reproducible model lifecycle management (experimentation, approval, deployment, monitoring, drift/evals, rollback), feature stores, CI/CD, and automated observability. 

• Drive GenAI adoption responsibly: identify high value use cases, build/reuse LLM platforms (RAG, vector search), set prompt/eval standards, safeguard IP/PHI, and manage content/usage policies and human in the loop controls. Much of this workstream will be in tight coordination with the office of our CISO.

• Deliver measurable business outcomes via data products and ML/AI solutions; prioritize the portfolio, define KPIs/OKRs with business owners, and track ROI, adoption, risk, and quality.

• Lead a multidisciplinary organization (data engineering, platform, analytics, data science/ML, data governance, AI product) with clear operating mechanisms, talent strategy, and succession plans.

• Partner with Security, Legal, Compliance, and Risk to implement privacy by design, model risk management, third party risk, and audit readiness; ensure encryption/IAM, data retention, and lineage are enforced. 

• Advance data quality: institute golden sources, data stewardship, data quality SLAs, and remediation workflows across domains. 

• Embed architecture standards and patterns: event-driven data, streaming (e.g., Kafka/Kinesis), APIs, data mesh/data product patterns, and zero ETL/ELT best practices. 

• Manage vendor and partner ecosystem: evaluate/contract platforms and model providers, negotiate commercial