Director, AI Systems Quality & Governance, R&D

Gilead Sciences•Foster City, CA

2d•$210,375 - $272,250•Hybrid

About The Position

The Director, AI Systems Quality & Governance, R&D will establish and operationalize a risk-based quality framework for artificial intelligence across Research & Development. Reporting to the Senior Director of eSystems Quality for R&D, this role defines how AI-enabled solutions are assessed, developed, tested, implemented, monitored, changed, and retired in a regulated environment. It ensures quality, data integrity, risk management, and appropriate controls are built into AI solutions by design, with documentation and evidence sufficient to demonstrate ongoing fitness for intended use and inspection readiness. The position establishes lifecycle standards, risk and control requirements, data quality expectations, testing and assurance strategies, post-implementation monitoring, and vendor evaluation practices for AI models and platforms. It also supports inspection readiness, builds AI quality capabilities through playbooks, templates, decision tools, and training, and provides product direction for an R&D Quality Intelligence Platform that connects and analyzes information across R&D Quality processes. Extensive experience in a Quality organization is not required. The role is suited to someone with experience in AI, data, digital products, technology risk, or controls who is willing to develop deeper expertise in GxP regulations and collaborate with colleagues experienced in Quality, validation, data integrity, and regulated R&D processes. This is an onsite position under Gilead’s G.Flex policy, with onsite work Tuesday through Thursday.

Requirements

BA/BS in computer science, data science, engineering, information systems, life sciences, or a related discipline and 12 or more years of relevant experience; or MA/MS in one of these fields and 10 or more years; or PhD in one of these fields and 8 or more years.
Significant experience in AI, machine learning, advanced analytics, digital products, data governance, technology risk, controls, or related fields.
Experience implementing, evaluating, or governing AI-enabled solutions in a complex enterprise environment.
Understanding of how AI models are developed, tested, deployed, monitored, changed, and retired.
Experience evaluating model performance, data quality, drift, human oversight, vendors, and operational controls.
Ability to translate technical risks into clear, implementable, and testable requirements.
Strong documentation, communication, facilitation, and stakeholder-management skills.
Willingness to develop deeper expertise in GxP regulations, Quality systems, validation, data integrity, and inspection readiness.

Nice To Haves

Experience within pharmaceutical, biotechnology, healthcare, medical device, financial services, or another regulated industry.
Familiarity with Clinical Operations, Regulatory Affairs, Patient Safety, Clinical Data Management, Medical Affairs, or related R&D processes.
Experience developing AI governance frameworks, lifecycle standards, model-risk frameworks, or control requirements.
Experience evaluating AI vendors, platforms, and embedded AI capabilities.
Familiarity with responsible AI, data lineage, model documentation, internal controls, validation, or risk-based assurance.
Product-management or product-owner experience.
Advanced degree in a technical, scientific, data, or business discipline.

Responsibilities

Design, implement, and maintain a risk-based AI Development Lifecycle for R&D, covering intake, intended use, risk classification, data readiness, development, testing, release, monitoring, change management, periodic review, and retirement.
Establish lifecycle documentation, evidence, reviews, approvals, decision points, and requirements aligned with applicable GxP regulations, Gilead procedures, data-integrity principles, and inspection expectations.
Develop a risk and control framework for AI-enabled solutions, addressing model performance, unreliable outputs, bias, data quality, drift, explainability, automation, human oversight, privacy, security, and vendor-controlled changes.
Define and establish requirements for human review, intervention, override, escalation, documentation, and demonstrating control effectiveness.
Collaborate with GxP Quality subject-matter experts to determine when formal change control, deviation, investigation, CAPA, or other Quality-system processes apply.
Define data requirements for training, configuring, testing, validating, and operating AI solutions, including a framework to assess accuracy, completeness, consistency, representativeness, lineage, provenance, timeliness, criticality, and fitness for intended use.
Ensure data sources, transformations, exclusions, assumptions, limitations, and traceability across source data, model versions, requirements, testing, outputs, and production use are documented.
Evaluate the use of patient, clinical, proprietary, and company data by internal models, external platforms, and vendors.
Establish risk-based testing and assurance strategies, including expectations for requirements, acceptance criteria, test data, methods, traceability, and objective evidence.
Ensure testing addresses accuracy, reliability, reproducibility, boundary conditions, failure scenarios, bias, human oversight, and real-world performance; determine when statistical, expert, comparative, deterministic, or production-monitoring approaches are appropriate.
Document model limitations, prohibited uses, expected failure modes, and required user actions.
Define post-implementation monitoring requirements, metrics, thresholds, alerts, escalation pathways, and triggers for investigation, retraining, additional testing, revalidation, suspension, or retirement.
Establish risk-based periodic reviews and clearly assign monitoring responsibilities and decision rights.
Assess risks and controls for open, closed, hosted, embedded, and vendor-managed AI models; review vendor documentation and determine whether the evidence supports Gilead’s intended use.
Identify additional testing, controls, contractual provisions, or monitoring required to address vendor evidence gaps, and define vendor notification expectations for updates, retraining, material changes, incidents, and performance issues.
Evaluate whether company data or user inputs may be retained or used to train external models.
Establish documentation and traceability expectations to support internal audits and health authority inspections; identify and address documentation, testing, and control gaps.
Help technical and business teams explain AI operation, risk controls, and how continued performance is demonstrated; apply lessons from audits, inspections, incidents, and implementation experience.
Build AI quality capabilities across R&D and eSystems Quality by developing playbooks, templates, decision tools, examples, and role-based training.
Communicate AI risks and assurance expectations to technical, business, Quality, and executive audiences, and help Quality colleagues apply GxP risk and control principles to AI-enabled solutions.
Develop personal knowledge of GxP regulations, validation, data integrity, and regulated R&D processes through collaboration with experienced team members.
Provide product direction for the R&D Quality Intelligence Platform, defining requirements for structured data, common taxonomies, data quality, traceability, and analytics.
Advance the R&D Quality Intelligence Platform from reporting toward Quality intelligence and decision support, including identification of emerging risks, recurring issues, control effectiveness, CAPA effectiveness, and areas requiring earlier intervention.
Ensure AI-enabled platform capabilities follow the established lifecycle, testing, monitoring, and governance requirements for other R&D AI solutions.
As a people leader, create inclusion, develop talent through coaching and feedback, empower teams, align their goals with organizational objectives, and help remove barriers.