Director, AI/ML Strategy and Technology Enablement

Takeda • Boston, MA

5h

About The Position

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.

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• 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.

Benefits

• U.S. based employees may be eligible to participate in medical, dental, vision insurance, a 401(k) plan and company match, short-term and long-term disability coverage, basic life insurance, a tuition reimbursement program, paid volunteer time off, company holidays, and well-being benefits, among others.
• U.S. based employees are also eligible to receive, per calendar year, up to 80 hours of sick time, and new hires are eligible to accrue up to 120 hours of paid vacation.

What This Job Offers

• Job Type: Full-time
• Career Level: Director
• Number of Employees: 5,001-10,000 employees