Associate Director, Advanced Analytics & AI Enablement

The Opportunity

Reporting to the Director, US Commercial Analytics, the Associate Director, Advanced Analytics & AI Enablement will own the specialist predictive and causal methods that improve targeting, promotional investment, and commercial decisions across Revolution Medicines' portfolio. As RevMed prepares for overlapping launches across multiple cancers, many priority HCPs are likely to be relevant to more than one brand, making it essential to determine which customers to prioritize, for which brands, and at what level of investment. This is a build role. You will establish patient identification and predictive targeting capability, stand up promotional-effectiveness and marketing-mix measurement, and define the model governance standards that keep methodological ownership inside RevMed. You will also partner on AI-enabled analytics products that move the organization toward trusted, self-service access to commercial data. This role is ideal for a hands-on quantitative leader who wants to see models activated in real workflows. This position is based out of our headquarters in Redwood City, CA.

Responsibilities

• Patient identification and predictive targeting: Develop and validate patient identification and treatment-progression models using claims, lab, and EHR-derived data; build HCP opportunity, propensity, and affinity scores that inform field and omnichannel targeting across brands.
• Promotional effectiveness and investment optimization: Own marketing-mix modeling, execution, and interpretation, including budget and allocation scenarios that inform portfolio promotional investment decisions.
• Attribution, causal inference, and incrementality: Lead studies that quantify the true commercial impact of promotional activity, applying appropriate causal methods and clearly communicating assumptions and uncertainty.
• Segmentation and next-best-action methods: Develop segmentation, affinity, and next-best-action approaches that support cross-brand customer prioritization where target populations overlap.
• Model governance and lifecycle management: Establish validation standards, monitoring scorecards, and refresh cadences for deployed models; decide when a model should scale, be retrained, or be retired based on performance and business impact.
• AI enablement for analytics: Partner on AI-enabled analytics use cases, including conversational analytics on commercial dashboards; support metric certification and semantic consistency so AI-delivered answers rest on governed definitions.
• Activation and measurement: Partner with Brand Analytics on use-case definition and with Omnichannel Analytics on how model outputs are activated in journeys and triggers and how intervention success is measured.
• Collaboration: Partner day-to-day with other analytics leads for routine campaign reporting, field and access dashboards, and production data engineering owned by Information Systems.
• Technical self-sufficiency and reuse: Work hands-on in SQL and Python notebooks to query, prototype, and productionize analyses in a modern cloud data platform environment.
• Other duties as assigned including travel as needed.

Required Skills, Experience and Education

• Bachelor's degree in a Science, engineering, or business discipline required, with 6+ years of progressive experience applying advanced analytics in Pharma/Biotech or a comparable commercial environment.
• Demonstrated ownership of predictive modeling and causal methods, including propensity and prediction models, attribution, incrementality, and marketing-mix modeling.
• Deep knowledge of pharmaceutical commercial data assets, including claims, lab, prescription, HCP and account, CRM, and digital engagement data.
• Strong hands-on technical skills in Python or R and SQL, with experience working in cloud analytics environments such as Databricks, Snowflake, or comparable platforms.
• Experience taking models from concept through validation, activation, and monitoring in real business workflows.
• Ability to explain method choice, assumptions, and uncertainty to non-technical commercial leaders and influence decisions accordingly.
• Extensive secondary analytical skills required; ability to translate data and insights into actionable recommendations for business growth.
• Strong problem solver with experience leveraging a variety of research and analytical vendors, study designs, and building quality vendor partnerships.
• Genuine enthusiasm for hands-on technical work: writing SQL and Python in notebook environments, working directly in a modern cloud data platform, and building reusable, well-documented analytical assets.
• Demonstrated use of generative AI tools to accelerate analytical work, such as conversational analytics agents, notebook and code assistants, or comparable capabilities.
• Entrepreneurial mindset, with a tolerance for ambiguity, and a passion for helping cancer patients.

Preferred Skills

• Advanced degree (MS, PhD, MBA, or equivalent) in statistics, data science, economics, engineering, or a related quantitative field, with 8+ years of progressive experience.
• Targeted oncology or specialty-therapeutics experience, particularly in biomarker-driven markets.
• Experience deploying next-best-action or decisioning frameworks in an omnichannel context.
• Familiarity with conversational analytics, semantic layers, or large language model-based analytics tooling.
• Experience with common commercial data and technology ecosystems such as IQVIA, Komodo, Veeva, Databricks, ThoughtSpot, or Power BI.