Clinical Data Science & AI Delivery Lead (Hybrid)

Gilead-Sciences; Raleigh (NC); Hybrid; USD 127,000 - 164,000; Full time

Job summary

Gilead Sciences Inc. in Raleigh, North Carolina, is seeking a Manager, Data Science & AI Delivery Lead to design, develop and deliver advanced analytics, ML and generative AI solutions across Clinical Development and Medical Affairs.

This role combines hands-on leadership with end-to-end delivery accountability in a hybrid setup. You will lead multidisciplinary teams, translate business and scientific problems into scalable data products, and ensure solutions are scientifically credible,

Qualifications

• Bachelor’s degree in data science, statistics, computer science, engineering, applied mathematics, bioinformatics, life sciences or a related discipline.
• 6+ years of relevant experience in data science or AI delivery.
• Experience with Python, SQL and data science libraries; ML/NLP/AI development.

Responsibilities

• Lead discovery with Clinical Development and Medical Affairs stakeholders to define analytical questions and success metrics.
• Prototype and productionize ML/NLP/AI solutions with rigorous evaluation and explainability.
• Oversee end-to-end delivery including roadmap, backlog, sprint planning, and release readiness.
• Ensure compliance with data governance, privacy, security and GxP requirements.
• Guide teams on analytic methods, reproducibility and responsible AI practices.

Skills

Data science leadership

Python/SQL

MLOps/data pipelines

Agile delivery

Stakeholder management

Education

Bachelor’s degree in data science or related field

Tools

Databricks

AWS

MLflow

Vector search

RAG/LLM evaluation

Job description

Gilead Sciences Inc. in Raleigh, North Carolina, is seeking a Manager, Data Science & AI Delivery Lead to design, develop and deliver advanced analytics, ML and generative AI solutions across Clinical Development and Medical Affairs.

This role combines hands-on leadership with end-to-end delivery accountability in a hybrid setup. You will lead multidisciplinary teams, translate business and scientific problems into scalable data products, and ensure solutions are scientifically credible,