Establish the AI Development Lifecycle

• Design, implement, and maintain a risk-based AI Development Lifecycle for R&D.
• Define requirements for intake, intended use, risk classification, data readiness, development, testing, release, monitoring, change management, periodic review, and retirement.
• Establish required documentation, evidence, reviews, approvals, and decision points across the lifecycle.
• Ensure that quality and control requirements are incorporated during solution design and development rather than added at the end.
• Align AI lifecycle requirements with applicable GxP regulations, Gilead procedures, data-integrity principles, and inspection expectations.

Define AI Risk and Control Requirements

• Develop a risk and control framework for AI-enabled solutions.
• Evaluate risks associated with model performance, unreliable outputs, bias, data quality, drift, explainability, automation, human oversight, privacy, security, and vendor-controlled changes.
• Define controls that can be implemented, tested, monitored, and supported by objective evidence.
• Establish requirements for human review, intervention, override, escalation, and documentation.
• Determine how control effectiveness will be demonstrated throughout the lifecycle.
• Work with GxP Quality subject-matter experts to determine when formal change control, deviation, investigation, CAPA, or other Quality-system processes apply.

Establish Data Quality Requirements

• Define requirements for data used to train, configure, test, validate, and operate AI solutions.
• Develop a framework for grading data based on accuracy, completeness, consistency, representativeness, lineage, provenance, timeliness, criticality, and fitness for intended use.
• Ensure that data sources, transformations, exclusions, assumptions, and limitations are documented.
• Define traceability expectations across source data, model versions, requirements, testing, outputs, and production use.
• Evaluate how patient, clinical, proprietary, and company data may be used by internal models, external platforms, and vendors.

Define AI Testing and Assurance Strategies

• Establish risk-based testing and assurance approaches for AI-enabled solutions.
• Define expectations for requirements, acceptance criteria, test data, test methods, traceability, and objective evidence.
• Ensure that test strategies address accuracy, reliability, reproducibility, boundary conditions, failure scenarios, bias, human oversight, and real-world performance.
• Define how teams will demonstrate that AI-generated outputs are correct, appropriate, and reliable for their intended use.
• Determine when statistical testing, expert review, comparative testing, deterministic testing, or production monitoring is appropriate.
• Ensure that model limitations, prohibited uses, expected failure modes, and required user actions are documented.

Establish Monitoring and Periodic Review

• Define requirements for monitoring AI solutions after implementation.
• Establish expectations for monitoring model performance, model drift, data drift, output quality, exceptions, human overrides, unintended outcomes, and vendor changes.
• Define meaningful metrics, thresholds, alert criteria, and escalation pathways.
• Establish triggers for investigation, retraining, additional testing, revalidation, suspension, or retirement.
• Define risk-based periodic review requirements focused on continued fitness for intended use.
• Ensure that monitoring responsibilities and decision rights are clearly assigned.

Evaluate AI Models, Platforms, and Vendors

• Assess the risks and controls associated with open, closed, hosted, embedded, and vendor-managed AI models.
• Evaluate vendor documentation related to model design, training data, performance, security, privacy, monitoring, and change management.
• Determine whether vendor evidence is sufficient to support Gilead’s intended use.
• Identify additional testing, controls, contractual provisions, or monitoring needed to address gaps.
• Define expectations for vendor notification of model updates, retraining, material changes, incidents, and performance issues.
• Evaluate whether company data or user inputs may be retained or used to train external models.

Ensure Inspection Readiness

• Establish documentation and traceability expectations for AI-enabled solutions.
• Ensure that intended use, risks, requirements, testing, approvals, limitations, monitoring, changes, and decisions are supported by retrievable evidence.
• Confirm that assurance activities are proportionate to risk and defensible during internal audits and health authority inspections.
• Help technical and business teams explain how AI solutions work, how risks are controlled, and how continued performance is demonstrated.
• Identify and address documentation, testing, or control gaps before inspection.
• Apply lessons learned from audits, inspections, incidents, and implementation experience to improve the AI Development Lifecycle.

Build AI Quality Capabilities

• Increase understanding of AI technologies, risks, and controls across R&D and eSystems Quality.
• Develop playbooks, templates, decision tools, examples, and role-based training.
• Explain AI risks and assurance expectations in language appropriate for technical, business, Quality, and executive audiences.
• Help Quality colleagues understand how established GxP risk and control principles apply to AI-enabled solutions.
• Continue to develop personal knowledge of GxP regulations, validation, data integrity, and regulated R&D processes through collaboration with experienced team members.

Provide Direction for the R&D Quality Intelligence Platform

• Provide product direction for a platform that connects and analyzes information across R&D Quality processes.
• Define requirements for structured data, common taxonomies, data quality, traceability, and analytics.
• Advance the platform from reporting toward Quality intelligence and decision support.
• Enable identification of emerging risks, recurring issues, control effectiveness, CAPA effectiveness, and areas requiring earlier intervention.
• Ensure that AI-enabled platform capabilities follow the same lifecycle, testing, monitoring, and governance requirements established for other R&D AI solutions.

We are all different, yet we all use our unique contributions to serve patients. Please see the following qualifications and skills we seek for this role.

U.S. Education & Experience

• BA/BS in computer science, data science, engineering, information systems, life sciences, or related discipline and 12+ years of relevant experience OR
• MA/MS in computer science, data science, engineering, information systems, life sciences, or related discipline and 10+ years of relevant experience OR
• PhD in computer science, data science, engineering, information systems, life sciences, or related discipline and 8+ years of relevant experience
• 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.

Preferred Qualifications

• 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 technical, scientific, data, or business discipline.

HOW YOU WILL KNOW YOU ARE A GOOD FIT

• You understand risk and controls and can determine whether they work in practice.
• You ask questions that expose assumptions, limitations, and potential failure points.
• You document requirements, decisions, risks, and evidence clearly.
• You can evaluate a technical solution without needing to be the person who built it.
• You can assess vendor claims and identify where additional evidence is needed.
• You balance attention to detail with an end-to-end, systems-level perspective.
• You are willing to learn from colleagues with deeper GxP and traditional Quality expertise.
• You believe quality should be built into AI solutions by design.
• You can help ensure that AI-enabled solutions are reliable, controlled, and inspection ready.

• Create Inclusion - knowing the business value of diverse teams, modeling inclusion, and embedding the value of diversity in the way they manage their teams.
• Develop Talent - understand the skills, experience, aspirations and potential of their employees and coach them on current performance and future potential. They ensure employees are receiving the feedback and insight needed to grow, develop and realize their purpose.
• Empower Teams - connect the team to the organization by aligning goals, purpose, and organizational objectives, and holding them to account. They provide the support needed to remove barriers and connect their team to the broader ecosystem.

The salary range for this position is: $210,375.00 - $272,250.00. Gilead considers a variety of factors when determining base compensation, including experience, qualifications, and geographic location. These considerations mean actual compensation will vary. This position may also be eligible for a discretionary annual bonus, discretionary stock-based long-term incentives (eligibility may vary based on role), paid time off, and a benefits package. Benefits include company-sponsored medical, dental, vision, and life insurance plans*.

https://www.gilead.com/careers/compensation-benefits-and-wellbeing

• Eligible employees may participate in benefit plans, subject to the terms and conditions of the applicable plans.

For more information about equal employment opportunity protections, please view the 'Know Your Rights' poster.

NOTICE: EMPLOYEE POLYGRAPH PROTECTION ACT

YOUR RIGHTS UNDER THE FAMILY AND MEDICAL LEAVE ACT

Gilead Sciences will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, (c) consistent with the legal duty to furnish information; or (d) otherwise protected by law.

Our environment respects individual differences and recognizes each employee as an integral member of our company. Our workforce reflects these values and celebrates the individuals who make up our growing team.

Gilead provides a work environment free of harassment and prohibited conduct. We promote and support individual differences and diversity of thoughts and opinion.

Please apply via the Internal Career Opportunities portal in Workday.