Manager, Data Science & AI Delivery Lead – Clinical Development and Medical Affairs

Gilead Sciences • Raleigh, NC

17d • $126,565 - $163,790 • Hybrid

About The Position

Gilead is seeking a Manager, Data Science & AI Delivery Lead to design, develop and deliver advanced analytics, machine learning and generative AI solutions across Clinical Development and Medical Affairs as part of its enterprise AI strategy. The role combines hands-on data science leadership with end-to-end delivery accountability, translating business and scientific problems into validated, scalable data products that are adopted and integrated into workflows.

The successful candidate will lead data exploration, feature engineering, model development, experimentation and evaluation, while coordinating multidisciplinary teams from discovery through production. They will partner with clinical, medical and technology stakeholders to ensure solutions are scientifically credible, technically robust, compliant, measurable and fit for use in a regulated context. The position is based in Raleigh, North Carolina, and is hybrid-eligible.

Requirements

• Bachelor’s degree in data science, statistics, computer science, engineering, applied mathematics, bioinformatics, life sciences or a related discipline and 6+ years of relevant experience; or a master’s degree and 4+ years of relevant experience.
• Hands-on experience developing statistical, machine learning, NLP or generative AI solutions using Python, SQL and commonly used data science libraries.
• Experience with supervised and unsupervised learning, experimental design, feature engineering, model evaluation, error analysis and interpretation of analytical results.
• Experience delivering data science products from problem definition and prototyping through production deployment, monitoring, adoption and enhancement.
• Working knowledge of data pipelines, cloud platforms, APIs, version control, automated testing, model lifecycle management and MLOps practices.
• Experience leading Agile delivery, including roadmap development, backlog prioritization, sprint planning, release coordination, dependency management and risk escalation.
• Ability to assess data fitness and communicate uncertainty, model limitations, technical trade-offs and recommendations clearly.
• Strong stakeholder management and communication skills across clinical, medical, scientific and technical audiences.

Nice To Haves

• Experience applying data science or AI in Clinical Development, clinical operations, clinical data management, biostatistics, safety, regulatory, quality or Medical Affairs.
• Experience with clinical trial, real-world, medical insights, publications, scientific literature or other life sciences datasets.
• Experience in a GxP-regulated environment and familiarity with model documentation, privacy, validation and responsible AI controls.
• Experience with Databricks, AWS, MLflow, vector search, retrieval-augmented generation or large language model evaluation.
• Familiarity with Veeva, clinical trial management systems, electronic data capture platforms, safety systems or CRM platforms.
• Experience leading vendors or co-sourced technical teams and establishing measurable adoption and business-value outcomes.

Responsibilities

• Lead discovery with Clinical Development and Medical Affairs stakeholders to define analytical questions, hypotheses, target outcomes, success measures and product requirements.
• Perform data exploration, preparation, feature engineering, statistical analysis and model prototyping using clinical, operational, medical and scientific datasets.
• Design, develop and evaluate machine learning, natural language processing and generative AI solutions appropriate to the business problem, data quality and regulated context.
• Establish experimentation and evaluation approaches, including baseline comparisons, error analysis, robustness testing, explainability, bias assessment and human-in-the-loop review.
• Partner with data engineering, MLOps, architecture, platform, security, privacy and quality teams to translate prototypes into production-ready solutions.
• Own end-to-end delivery of assigned data science and AI products, including scope, roadmap, backlog, sprint execution, dependencies, release readiness, adoption and continuous improvement.
• Develop epics, user stories, acceptance criteria and validation evidence connecting model performance to user needs and measurable business or scientific outcomes.
• Guide data scientists and delivery partners on analytical methods, coding quality, reproducibility, documentation, model evaluation and responsible AI practices.
• Support development and monitoring of data pipelines, feature stores, model services, APIs and analytical applications using approved enterprise platforms.
• Define and track data quality, model performance, drift, reliability, user adoption, cycle-time improvement and value-realization metrics.
• Ensure solutions comply with applicable data governance, privacy, security, validation and GxP requirements.
• Maintain model documentation, experiment records, technical specifications, operating procedures and production-support runbooks.
• Manage vendors and co-sourced teams, remove delivery blockers, escalate risks, and communicate technical trade-offs and progress to stakeholders and leadership.

Benefits

• Discretionary annual bonus eligibility
• Discretionary stock-based long-term incentives (eligibility may vary based on role)
• Paid time off
• Company-sponsored medical insurance
• Company-sponsored dental insurance
• Company-sponsored vision insurance
• Company-sponsored life insurance
• Benefits package

What This Job Offers

• Job Type: Full-time
• Career Level: Manager