Senior Data Scientist, Advanced Risk Modeling

Oscar Health•San Francisco, CA

4h•Remote

About The Position

The Senior Data Scientist, Advanced Risk Modeling, is a senior individual contributor role focused on utilizing advanced machine learning (ML) and artificial intelligence (AI) methodologies on modern ML platforms. This role will build and deploy sophisticated models to help solve complex business problems specifically within Oscar's risk adjustment domain. You will report into the Director of Data. Work Location: This is a remote position, open to candidates who reside in: San Francisco, CA. You will be fully remote; however, our approach to work may adapt over time. Future models could potentially involve a hybrid presence at the hub office associated with your metro area. #LI-Remote

Requirements

4+ years of industry or other quantitative technical fields (which may include academia).
3+ years of work experience working with SQL, R, and/or Python to query, manipulate and analyze data
3+ years experience building data models, using more advanced analytics methods, statistical modeling, and/or data processing

Nice To Haves

Advanced degree in a quantitative or technical field
Experience in the healthcare, finance and/or insurance industries

Responsibilities

Conduct in-depth data analysis and advanced feature engineering using large, complex healthcare datasets to create robust predictors.
Design, develop, and validate advanced ML/AI models (e.g., regression/classification, deep learning, NLP, gradient boosting, LLM experimentations) to solve complex clinical and business problems within the risk adjustment domain.
Leverage modern ML Ops platforms (e.g., Vertex AI) and data science best practices to efficiently train, test, deploy, and maintain production-level models.
Build and maintain the supporting data systems, analytics, and monitoring infrastructure required for complex, production-level AI/ML systems.
Research, develop, and evaluate new AI applications using cutting-edge techniques, including LLM models and agentic frameworks, to solve critical business challenges in risk adjustment.
Work closely with business stakeholders, engineering teams, and product managers to gain a deep understanding of business operations and technical systems.
Build strong partnerships to foster close, effective integrations between data science, engineering, product, and business units.
Partner with leaders to identify high-impact opportunities, translate complex business challenges into data science solutions, and ensure successful model implementation within business workflows.