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

Gilead Sciences•Raleigh, NC

19d•$126,565 - $163,790•Hybrid

About The Position

The Manager, Data Science & AI Delivery Lead designs, develops, and delivers advanced analytics, machine learning, and generative AI solutions across Clinical Development and Medical Affairs. 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 position partners with clinical, medical, scientific, and technology stakeholders and coordinates multidisciplinary teams from discovery through production. It ensures that solutions are scientifically credible, technically robust, compliant, measurable, and responsive to user needs. The role 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 clearly communicate uncertainty, model limitations, technical trade-offs, and recommendations.
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, data 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 suited to business problems, data quality, and regulated contexts.
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 move 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 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.