Director Data AI & Analytics

** Position Overview*
* ** Position Description*
* We are hiring a Director Data, AI and Analytics. The Director Data, AI and Analytics will own the strategy, platforms, operating model, and outcomes for Data, AI, and Analytics across three FUJIFILM life‑sciences businesses. In highly regulated GxP environments, deliver a trusted data backbone, governed self‑service analytics, advanced analytics, bioinformatics pipelines, and agentic AI-meeting 21 CFR Part 11/EU Annex 11, ALCOA+, and CSV/CSA expectations.

Lead a cross‑site team (CoE + domain squads), partner with QA/CSV, Security, Lab IT, M&Q Systems, and ERP, and convert data and AI into measurable business value.

** This position is hybrid in Irvine, CA.*
* ** Company Overview*
* At FUJIFILM Biosciences, we turn curiosity into breakthroughs that advance the field of life sciences. By offering a comprehensive portfolio of products and services in cell culture media, discovery research reagents, recombinant growth factors and proteins, fine chemicals, and critical assay materials, we partner with the brightest minds in biotech and pharma to tackle the world's biggest health challenges.

Imagine being part of a team that enables life-changing discoveries like new vaccines, therapies, and advancements in regenerative medicine. Our culture fosters curiosity, collaboration, and innovation, and we pride ourselves on delivering unparalleled quality and service to our partners. If you're ready to make an impact, your future belongs with us at FUJIFILM Biosciences.

Our headquarters in Santa Ana, California, is surrounded by world-class schools, lush parks, and scenic beaches like Laguna and Huntington, making it an ideal place to live, work, and explore. With additional campuses across the US, Europe, China, and Japan, we offer opportunities to make a difference worldwide.

Fujifilm is globally headquartered in Tokyo with over 70,000 employees across four key business segments of healthcare, electronics, business innovation, and imaging. We are guided and united by our Group Purpose of "giving our world more smiles." Visit:

** Job Description*
* ** Responsibilities:*
* + Define the multi‑year Data/AI roadmap for locations in California and Wisconsin; align to business priorities and compliance needs.

+ Stand up a Data, AI & Analytics Center of Excellence with domain‑aligned product squads (M&Q, R&D/Bioinformatics, Finance/SC).

+ Run governance:
Data & AI Steering Committee, Model Risk & Compliance Board; assign data product ownership.

+ Own the enterprise data platform (lakehouse/warehouse), ingestion (ELT/CDC), orchestration, and semantic/metric layers.

+ Implement catalog/lineage, data quality rules, access controls; drive data product SLAs and ownership.

+ Integrate core systems: MES (e.g., PAS‑X), QMS (ETQ/Veeva), LIMS/ELN (Lab Ware/Benchling), ERP (e.g., SAP/Oracle), OSI PI/AVEVA, and instrument data hubs.

+ Deliver executive and operational analytics (FPY/yield, OEE, deviation/CAPA TAT, batch release, QC throughput, inventory turns, cost‑to‑serve).

+ Enable governed self‑service (certified datasets/semantic models, KPI library, training, community of practice).

+ Lead data science programs for manufacturing quality, supply chain, and R&D.

+ Own bioinformatics analytics and pipelines (e.g., NGS/omics workflows, imaging pipelines, assay analytics) and integration with lab systems and data platforms.

+ Build responsible and agentic AI capabilities (assistants/co‑pilots, retrieval/grounding, task automation) with MLOps (experiment tracking, model registry, CI/CD, monitoring, retraining). Separate GxP vs non‑GxP AI; ensure intended use, explainability, validation/CSA, and periodic review for regulated models.

+ Embed compliance by design: 21 CFR Part 11, Annex 11, ALCOA+, data retention/archival, audit trails.

+ Partner with QA/CSV on risk‑based validation, test evidence, and inspections; maintain validated state for applicable analytics/data flows.

+ Coordinate with Corporate Security on classification, RBAC/least privilege, encryption, logging/monitoring, and incident response for data/AI workloads.

+ Govern AI safety: model cards, red‑team testing, bias/fairness…