Data Scientist, Oscar Health

• 3+ years of experience in data science, applied analytics, or a related quantitative field (industry, academia, or both)
• 2+ years of experience using SQL and Python and/or R to query, analyze, and manipulate data
• 2+ years of experience building data models, using more advanced analytics methods, statistical modeling, and/or data processing

Desirable

• Oscar's data team is focused on pushing our understanding of this complex landscape of health care and insurance business. Insurance companies sit on a trove of data that is both broad and deep, spanning financial claims, clinical medical records, and rich product interaction data from our members. Connecting the dots across these datasets gives us unique insight into how the healthcare system functions and allows us to improve care coordination, better manage risk, build higher-performing provider networks, and create a standout consumer experience in our product
• As a Data Scientist II you will drive data science projects across multiple teams and domains. In this role, you’ll take ownership of analytical and modeling work, contribute to technical direction, and partner closely with business, engineering, product and data stakeholders to deliver impactful solutions
• This is a mid-level role for data scientists who can operate independently, manage ambiguity, and apply advanced analytics or modeling techniques with minimal oversight
• ​​You will report into a leader on the Data team (Senior Data Scientist and above). This posting is representative of multiple roles, and final team placement will be determined during the interview process
• Research, develop, and maintain data pipelines, statistical models, and advanced analytical solutions
• Own projects end-to-end, including problem definition, implementation, validation, and iteration
• Deliver high-quality analytical and modeling outputs with limited manager oversight
• Collaborate closely with data science peers and cross-functional partners across business units
• Help ensure data science processes and outputs align with broader team strategies and roadmaps
• Provide guidance or informal mentorship to junior data scientists or analysts when needed
• Contribute to improving data science practices, tooling, and documentation