Senior Data Scientist, Oscar Health

• 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

Desirable

• Hi, we're Oscar. We're hiring a Senior Data Scientist to join our Data Team
• 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
• 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