Senior Data Scientist, Pharmacy

Oscar Health•New York, NY

3d•$163,944 - $215,177•Hybrid

About The Position

The Senior Data Scientist, Pharmacy supports Oscar’s pharmacy benefit by developing data pipelines, models, and analytics to improve medication access, member experience, and plan affordability. The role leads projects in formulary optimization, utilization and population-need trend analytics, and prior authorization automation, in partnership with clinical, business, product, and engineering teams. The position designs and develops clinical AI and machine-learning applications, leading experimental design, target development, and operationalization. It applies rigorous data science practices to measure performance, prioritize safety and observability, and guide deployment decisions using clinical and operational success criteria. It also uses advanced analytics, models, and scenario analyses to evaluate financial and health outcomes, quantify tradeoffs and uncertainty, test assumptions, and provide actionable recommendations. The role validates analytical methods and findings, collaborates with data leaders and business teams to improve data processes, and partners with business and technical stakeholders to prioritize opportunities, define success metrics, and guide experiments through evaluation and implementation. The role is based in Oscar’s New York City office with a hybrid schedule requiring three in-office days per week. Thursdays are required in-office; the other two office days are flexible. Base pay is $163,944–$215,176.50 per year. The position is also eligible for employee benefits, Oscar’s unlimited vacation program, company equity grants, and annual performance bonuses.

Requirements

Bachelor’s degree or at least 6 years of industry or educational experience in a technical field.
At least 4 years of work experience using SQL, R, and/or Python to query, manipulate, and analyze data.
At least 4 years of experience building data models and using advanced analytics methods, including in-depth analysis rather than simple descriptive analysis.
At least 4 years of experience with statistical modeling and data processing/ETL pipeline building.
At least 2 years of experience working with and presenting to non-technical stakeholders.

Nice To Haves

Experience in healthcare, finance, or insurance, particularly pharmacy benefits or clinical operations.
Experience developing and deploying AI/ML applications involving unstructured documents, with rigorous evaluation and production monitoring.
Experience using statistical modeling to evaluate utilization trends, program effectiveness, or financial and health outcome tradeoffs, including quantifying uncertainty.
Master’s degree in a quantitative field plus 3 years of relevant experience, or a PhD in a quantitative field.

Responsibilities

Design and develop clinical AI and machine-learning applications, leading the experimental lifecycle, target development, and operationalization strategy.
Apply rigorous data science practices to measure performance, prioritize safety and observability, and ground deployment decisions in clinical and operational success criteria.
Use advanced analytics to evaluate complex financial and health outcome questions related to clinical policies, benefit design, and member programs.
Develop models and scenario analyses to quantify tradeoffs, test assumptions, characterize uncertainty, and translate findings into actionable recommendations.
Validate analytical methods and results to ensure conclusions are robust and appropriate for the decisions they inform.
Collaborate with data leaders and business teams to improve data processes and lead execution with the team.
Understand stakeholder needs, provide data support, and work together to develop solutions that improve processes and outcomes.
Partner with business and technical stakeholders to prioritize opportunities, define success metrics, and guide experiments through evaluation and implementation.
Comply with all applicable laws and regulations.
Perform other duties as assigned.