AI Principal Engineer - Clinical Data Analytics
Exelixis
August 19, 2026
On-site
Alameda, CA
IT
Summary/Job Purpose:
The AI Principal Engineer - Clinical Data Analytics leads building and scaling data engineering and AI capabilities across the Development organization in a regulated environment, partnering with Data Science and Statistics to deliver actionable, outcome-driven insights using Generative/Agentic AI.
Essential Duties/Responsibilities:
- Develop production AI/ML and Generative AI solutions (evaluation, deployment, monitoring, lifecycle management).
- Identify, scope, prioritize, and deliver high-impact analytics and AI use cases with business leaders.
- Lead MLOps/LLMOps (model deployment, prompt/version management, monitoring, governance).
- Standardize reusable data models, semantic layers, and APIs for downstream analytics/AI.
- Translate business questions into data and analytics solutions; enable data-driven decision-making via harmonized metrics/KPIs.
- Explain technical concepts to non-technical stakeholders; ensure understanding of model performance, efficiency, and explainability.
- Establish prompt engineering, AI usage training, and model governance best practices.
- Collaborate with Legal, Compliance, and Security to meet regulatory/ethical standards.
- Design, implement, and evolve modern data architectures (warehouses, lakes, pipelines); ensure scalable, reliable, high-performance platforms.
- Evaluate, select, and optimize data/analytics/AI technologies.
Education/Experience:
- Bachelor’s degree (Computer Science/Data related) + 11 years; or Master’s degree + 9 years; or equivalent combination.
Required Qualifications/Skills:
- Advanced Databricks, Unity Catalog, Delta Lake, lakehouse governance.
- Advanced Python and SQL (R as needed).
- Intermediate clinical-domain experience delivering data acquisition/analytics/AI.
- Knowledge of Delta Lake/data modeling best practices and Unity Catalog metadata/lineage.
- Data governance and cataloging tools (e.g., Atlan/Colibra/Reltio).
- BI platform development/administration (Spotfire, Tableau, Power BI).
- AWS SaaS infrastructure (S3/SE, EC2, Lambda, Glue, Redshift).
- CI/CD and version control for pipelines; data modeling/warehouse architecture; DevOps/data governance.
- Experience with classified sensitive data and analytical products; GxP system ownership.
- Databricks maintenance/monitoring strategies.
- Pharmaceutical/CRO clinical data strategy and insights experience.
- Strong communication and organizational/time management.
Travel Requirements:
- 30% global travel.
The AI Principal Engineer - Clinical Data Analytics leads building and scaling data engineering and AI capabilities across the Development organization in a regulated environment, partnering with Data Science and Statistics to deliver actionable, outcome-driven insights using Generative/Agentic AI.
Essential Duties/Responsibilities:
- Develop production AI/ML and Generative AI solutions (evaluation, deployment, monitoring, lifecycle management).
- Identify, scope, prioritize, and deliver high-impact analytics and AI use cases with business leaders.
- Lead MLOps/LLMOps (model deployment, prompt/version management, monitoring, governance).
- Standardize reusable data models, semantic layers, and APIs for downstream analytics/AI.
- Translate business questions into data and analytics solutions; enable data-driven decision-making via harmonized metrics/KPIs.
- Explain technical concepts to non-technical stakeholders; ensure understanding of model performance, efficiency, and explainability.
- Establish prompt engineering, AI usage training, and model governance best practices.
- Collaborate with Legal, Compliance, and Security to meet regulatory/ethical standards.
- Design, implement, and evolve modern data architectures (warehouses, lakes, pipelines); ensure scalable, reliable, high-performance platforms.
- Evaluate, select, and optimize data/analytics/AI technologies.
Education/Experience:
- Bachelor’s degree (Computer Science/Data related) + 11 years; or Master’s degree + 9 years; or equivalent combination.
Required Qualifications/Skills:
- Advanced Databricks, Unity Catalog, Delta Lake, lakehouse governance.
- Advanced Python and SQL (R as needed).
- Intermediate clinical-domain experience delivering data acquisition/analytics/AI.
- Knowledge of Delta Lake/data modeling best practices and Unity Catalog metadata/lineage.
- Data governance and cataloging tools (e.g., Atlan/Colibra/Reltio).
- BI platform development/administration (Spotfire, Tableau, Power BI).
- AWS SaaS infrastructure (S3/SE, EC2, Lambda, Glue, Redshift).
- CI/CD and version control for pipelines; data modeling/warehouse architecture; DevOps/data governance.
- Experience with classified sensitive data and analytical products; GxP system ownership.
- Databricks maintenance/monitoring strategies.
- Pharmaceutical/CRO clinical data strategy and insights experience.
- Strong communication and organizational/time management.
Travel Requirements:
- 30% global travel.