Director Data AI & Analytics

Fujifilm•Irvine, CA

2d•$154,565 - $216,100•Hybrid

About The Position

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.

Requirements

BS/BA (or equivalent) in Business, Computer Science or similar.
10+ years leading Data/Analytics functions with 5+ years in regulated life sciences (biotech, pharma, CDMO); multi‑site/global experience.
Ownership of enterprise data platforms (lakehouse/warehouse), analytics programs, and MLOps in production.
Strong GxP/CSV/CSA knowledge with practical application in analytics/AI and data flows; 21 CFR Part 11/Annex 11/ALCOA+.
Hands‑on familiarity with bioinformatics analytics/pipelines (e.g., NGS/omics, imaging, workflow orchestration) and integration with LIMS/ELN and lab systems.
Proven record converting Data/AI programs into business outcomes (e.g., cycle time, yield/FPY, cost, quality).
Cross‑functional leadership with QA/CSV, Manufacturing/Quality, R&D, Security, ERP/MES/QMS owners, and vendors.
Excellent executive communication and pragmatic technical guidance.
Platforms: Databricks/Snowflake/BigQuery; Azure/AWS data services; Spark; DBT; Airflow/Prefect; Kafka/EventHub.
BI: Power BI/Tableau; semantic/metrics layers; KPI governance.
MLOps/AI: MLflow/W&B, SageMaker/Azure ML; vector DBs/RAG; LLM orchestration; agent frameworks; feature stores.
Bioinformatics: Nextflow/Snakemake, CWL/WDL; common tools (e.g., GATK) or imaging/omics pipelines; HPC/containers.
Governance: Collibra/Alation/Atlan; model risk management/AI governance.
Certifications: GAMP 5/CSA, ITIL, cloud (Azure/AWS), or data governance certifications.
Ability to speak, read, and write in English.
Good verbal and written communication skills; effective presentation skills.

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 (LabWare/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 checks, PHI/PII safeguards, vendor due diligence.
Prioritize a portfolio of use cases with quantified value; pilot, scale winners, retire low‑value work.
Publish a quarterly “Data & AI Outcomes” report (business impact, adoption, cost/performance, compliance posture).
Select and govern platform vendors (cloud, data/ML, BI, bioinformatics tools) and SIs; negotiate commercials, SLAs, exit terms.
Drive FinOps: usage visibility, rightsizing, tiered storage, and cost/performance optimization.
Hire and develop data/platform/analytics engineers, data scientists/ML engineers, bioinformatics engineers, and product managers.
Build cross‑site career paths and a culture of reliability, compliance, and measurable impact.