Hi, we're Oscar. We're hiring a VP of Data Science (Core Insurance) to join our Data Science team.

Oscar is the first health insurance company built around a full stack technology platform and a relentless focus on serving our members. We started Oscar in 2012 to create the kind of health insurance company we would want for ourselves—one that behaves like a doctor in the family.

About the role:

This leadership position is responsible for directing all Data Science efforts centered on Oscar's core insurance operations. The primary focus is on utilizing advanced predictive modeling and AI to enhance critical insurance functions and spur innovation in the management and delivery of healthcare.

You will report into the SVP, Data.

Work Location: This position is based in our New York office, requiring a hybrid work schedule with 3 days of in-office work per week. Thursdays are a required in-office day for team meetings and events, while your other two office days are flexible to suit your schedule. #LI-Hybrid

Pay Transparency: The base pay for this role is: $290,628 - $381,449 per year. You are also eligible for employee benefits, participation in Oscar's unlimited vacation program, company equity grants and annual performance bonuses.

Responsibilities:

• Core Insurance Data Science Strategy: Define and execute the strategy for utilizing data science and AI to optimize key insurance functions, including risk adjustment, underwriting, claims management, fraud detection, and actuarial modeling, with a focus on solving end to end consumer and business problems, in partnership with engineering, product, and business stakeholders.
• AI and Innovation Leadership: Spearhead the application of cutting-edge AI and machine learning models (e.g., deep learning, large language models) to solve complex problems in the healthcare and insurance space, driving innovation for the future of health care. This includes identifying and building full consumer facing features.
• Model Development and Deployment: Lead the full lifecycle of data science projects, from hypothesis generation and experimentation to the robust deployment and maintenance of production-grade models in partnership with the engineering teams.
• Business Impact and Value: Work closely with business leaders (e.g., Actuarial, Finance, Operations) to translate complex data science results into tangible business value, improving efficiency, reducing costs, and enhancing member experience.
• Platform Collaboration: Collaborate with the VP of Analytics and the broader engineering organization to ensure the data science team has access to the necessary data infrastructure, tools, and MLOps capabilities.
• Team Building: Build and manage a team of expert data scientists and machine learning engineers, fostering a culture of rigorous scientific inquiry, continuous learning, and measurable business impact.
• Compliance with all applicable laws and regulations
• Other duties as assigned

Requirements:

• Minimum: Bachelor’s degree in a quantitative or technical field (e.g., Data Science, Statistics, Computer Science, Economics, Mathematics, or Engineering).
• Leadership Breadth: 10–15+ years of experience in data, analytics, or insights, with at least 5–7 years in a senior leadership role (Director or VP level).
• Function Buildout: Proven track record of building and scaling a centralized analytics or data science function from the ground up, ideally in a mid-to-large-scale organization.
• Healthcare Domain: 5+ years of experience in the healthcare industry. Must have a deep understanding of clinical data, health-tech operations, and the regulatory environment (HIPAA, GDPR, etc.).
• Technical Ownership: Experience managing the full data stack, including collaboration with Engineering on data architecture, platform scalability, and analytics engineering (e.g., Snowflake, dbt, Databricks).
• Insights & Research Oversight: Demonstrated experience overseeing qualitative and quantitative research functions, specifically managing User Research (UXR) and Market Research teams to drive product/strategy decisions.
• AI/ML Implementation: Practical experience leading teams that have successfully deployed AI/Machine Learning models into production environments to solve business problems (e.g., predictive modeling for patient outcomes or operational efficiency).

Bonus points:

• Preferred: Master’s degree or PhD in Data Science, Health Informatics, MBA with a technical concentration, or a related quantitative field. An advanced degree is highly preferred given the "Innovation via AI" and "Insights/Research" components.
• Strategic Storytelling: Ability to translate complex data and AI concepts into compelling narratives for executive stakeholders and Board-level presentations.
• Platform Acumen: Strong familiarity with modern data stack (MDS) tools and "Platform-as-a-Product" thinking.