Associate Director, Advanced Analytics & AI Enablement

Revolution Medicines•Redwood City, CA

11d•Onsite

About The Position

The Associate Director, Advanced Analytics & AI Enablement will own specialist predictive and causal methods to improve targeting, promotional investment, and commercial decisions across Revolution Medicines’ portfolio. Reporting to the Director, US Commercial Analytics, this role will establish patient identification and predictive targeting capabilities, develop promotional-effectiveness and marketing-mix measurement, and define model governance standards that preserve methodological ownership within the company. The role will also partner on AI-enabled analytics products that support trusted, self-service access to commercial data. This is a hands-on build role for a quantitative leader who wants to activate models in real workflows. The position is based at the company’s headquarters in Redwood City, California, and includes travel as needed. The role partners with Brand Analytics, Omnichannel Analytics, Information Systems, and other analytics leads to define use cases, activate models, measure interventions, and support routine reporting, dashboards, and production data engineering.

Requirements

Bachelor’s degree in a science, engineering, or business discipline.
At least 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 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 choices, assumptions, and uncertainty to non-technical commercial leaders and influence decisions.
Extensive secondary analytical skills and ability to translate data and insights into actionable recommendations for business growth.
Strong problem-solving skills and experience leveraging research and analytical vendors, study designs, and building quality vendor partnerships.
Enthusiasm for hands-on technical work, including 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, tolerance for ambiguity, and passion for helping cancer patients.

Nice To Haves

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 commercial data and technology ecosystems such as IQVIA, Komodo, Veeva, Databricks, ThoughtSpot, or Power BI.

Responsibilities

Develop and validate patient identification and treatment-progression models using claims, lab, and EHR-derived data.
Build HCP opportunity, propensity, and affinity scores to inform field and omnichannel targeting across brands.
Own marketing-mix modeling, execution, and interpretation, including budget and allocation scenarios for portfolio promotional investment decisions.
Lead studies that quantify the commercial impact of promotional activity using appropriate causal methods, and clearly communicate assumptions and uncertainty.
Develop segmentation, affinity, and next-best-action approaches to support cross-brand customer prioritization where target populations overlap.
Establish model validation standards, monitoring scorecards, and refresh cadences; determine when models should scale, be retrained, or be retired based on performance and business impact.
Partner on AI-enabled analytics use cases, including conversational analytics on commercial dashboards, and support metric certification and semantic consistency.
Partner with Brand Analytics on use-case definition and with Omnichannel Analytics on activating model outputs in journeys and triggers and measuring intervention success.
Collaborate with other analytics leads on routine campaign reporting, field and access dashboards, and production data engineering owned by Information Systems.
Work hands-on in SQL and Python notebooks to query, prototype, and productionize analyses in a modern cloud data platform environment.
Perform other duties as assigned, including travel as needed.