Associate Director, Advanced Analytics & AI Enablement

Revolution Medicines•Redwood City, CA

1d•$186,000 - $233,000•Onsite

About The Position

Revolution Medicines is seeking a hands-on quantitative leader to establish predictive and causal analytics capabilities that improve targeting, promotional investment, and commercial decisions across its oncology portfolio. Reporting to the Director, US Commercial Analytics, this role will address cross-brand customer prioritization as the company prepares for overlapping launches across multiple cancers. The position will build patient-identification and predictive-targeting capabilities, establish promotional-effectiveness and marketing-mix measurement, and set model-governance standards that keep methodological ownership within Revolution Medicines. The Associate Director will also partner on AI-enabled analytics products that support trusted, self-service access to commercial data. This is a hands-on role focused on bringing models into real business workflows, including work in SQL and Python notebooks in a modern cloud data platform environment. The position is based onsite at the company's headquarters in Redwood City, California, with travel as needed.

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.
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.
Extensive secondary analytical skills and ability to translate data and insights into actionable recommendations for business growth.
Experience leveraging a variety of research and analytical vendors and 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 attribution, causal-inference, and incrementality studies to quantify the commercial impact of promotional activity and communicate assumptions and uncertainty.
Develop segmentation, affinity, and next-best-action approaches that support cross-brand customer prioritization where target populations overlap.
Establish model validation standards, monitoring scorecards, and refresh cadences; determine when deployed 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 activation of model outputs in journeys and triggers and measurement of intervention success.
Collaborate with 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.