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Job Description

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.

Objectives / Purpose

• 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.

Scope / Impact

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.

Accountabilities

Strategy & Roadmap

• 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.

Platform, Data & Integration

• 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.

Agentic AI & Productization

• 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).

Adoption & Change Enablement

• 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.

Governance, Risk & Compliance

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

Impact & Reporting

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

Qualifications

Required

• Advanced degree in Computer Science, AI/ML, Computational Biology/Chemistry, Bioinformatics, or related; or equivalent industry experience.
• 10+ years in AI/ML for life sciences; 5+ years strategic leadership delivering production AI in scientific environments.
• 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.

Preferred

• 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 m