Director, AI​/ML Strategy and Technology Enablement

Role Summary

Lead the strategy, platform build-out, and adoption of AI/ML across Research for global digital transformation effort, making AI agents, models, and tools a daily, accessible part of wet‑lab and dry‑lab scientists’ workflows. Translate AF priorities into a practical, compliant AI services layer—data foundations, MLOps, agentic assistants, model governance, and change enablement—that shortens time from experiment to insight and elevates decision quality across discovery programs.

• Define and execute a multi‑year AI/ML roadmap aligned to Research use cases and KPIs.
• Establish an AI‑ready data foundation (FAIR-by-design) and scientist‑facing AI tools embedded in ELN/LIMS/instrument workflows.
• Institutionalize Responsible AI & GxP‑aware governance for production models.
• Drive adoption through super‑user networks, training, and change management to achieve measurable value and ROI.

Global Research scope with cross‑site collaboration (US/EU/JP). Direct impact on data‑to‑decision latency, assay/analysis reproducibility, and portfolio productivity. Partner with operations, Computational Sciences & Data Strategy, IT, function leads, and platform teams to deliver outcomes at scale.

• Own Research’s AI/ML strategy and sequencing (MVP → scale) across wet‑lab ↔ dry‑lab integration and self‑service tools.
• Align priorities with Research’s KPIs and portfolio goals; establish and monitor achievement of success criteria and milestones.

• Guide the development of AI‑ready data foundations (provenance, metadata/ontologies, harmonization) across ELN/LIMS, instruments, imaging, and omics.
• Integrate platforms (e.g., ELN, SDMS & AI Cloud) to liberate, contextualize, and operationalize lab data for AI/ML.
• Stand up modern MLOps (CI/CD, registries, experiment tracking, monitoring) and secure service/APIs embedded in workflows.

• Design self‑service and user‑friendly processes for deployment of AI agents for scientists (literature triage, protocol assist, data QC, analysis pipelines, code helpers).
• Guide engineering efforts to deliver production models (e.g., sequence/structure prediction, assay QC, outlier detection, multimodal analytics).

• Lead adoption via super‑user networks, training, and communications; co‑own readiness plans with NCSP.
• Work with Change Management leads to publish playbooks and guardrails enabling self‑service AI workflows for scientists.

• Define and Implement Responsible AI and risk‑based governance (ALCOA+, validation mindset, audit trails, XAI, privacy/PII controls).

• Own measurable impact (adoption, latency, reproducibility, ROI) and provide transparent reporting to R&D leadership and key stakeholders.

• PhD degree in a scientific discipline with 10+ years experience, or
• MS with 16+ years experience, or BS with 18+ years experience (preferably with an advanced degree in Computer Science, AI/ML, Computational Biology/Chemistry, Bioinformatics, or related; or equivalent industry experience.)
• Proven MLOps platform build and delivery of scientist‑facing AI tools embedded in ELN/LIMS/instrument workflows.
• Expertise in FAIR data, scientific data models/ontologies, and integration across wet‑lab instruments, imaging, and omics.
• Experience with Responsible AI and GxP‑adjacent validation/governance in pharma/biotech R&D.
• Strong stakeholder management; ability to translate complex science/data into usable AI for end users.

• Experience working in wet-labs and knowledge of Research and Development workflows and processes in either the biologics and/or small molecule fields.
• Agentic AI systems and LLMs for scientific contexts; multimodal ML (text/images/sequences/numerical).
• Knowledge of Research/Pharma Sci common data models and cloud analytics/HPC integrations.

We understand compensation is an important factor as you consider the next step in your career. We are committed to equitable pay for all employees, and we strive to be more transparent with our pay…