Staff Data Scientist (SME: Healthcare Financial Risk & Underwriting)

Prealize Health is a predictive analytics company that leverages machine learning and clinical expertise to help patients obtain better care, sooner. The Staff Data Scientist will serve as the technical authority for healthcare financial risk and underwriting modeling, leading the evolution of predictive models and collaborating with cross-functional teams to align technical capabilities with market opportunities.

Responsibilities:

• Act as the Subject Matter Expert (SME), drive the end-to-end building and execution of our Risk & Underwriting AI models, directly translating actuarial and healthcare financial risk expertise into high-performance AI models and rigorous evaluation frameworks
• Set the technical roadmap and lead the development of ML models focused on healthcare financial risk stratification and financial underwriting
• Establish best practices for model building, model validation, and rigorous back testing/benchmarking across the data science organization to drive high performance models, while maintaining stability and alignment of models
• Mentor junior data scientists, fostering a culture of rigorous research, high-quality engineering, and innovation
• Partner with clinicians to translate medical knowledge into data-driven hypotheses and with Product/Executive teams to align technical capabilities with market opportunities
• Work with Engineering to design the next generation of our platform’s quality and extensibility, ensuring it remains performant for millions of patients
• Represent Prealize Health’s technical expertise to external strategic partners and stay at the forefront of literature in ML, Actuarial Science, and Biostatistics

Requirements:

• PhD and/or MS in Statistics, Biostatistics, Economics, Computer Science, or a related quantitative field
• 8–10+ years of experience building and deploying commercial-grade data science products with a proven track record of technical leadership
• Deep experience building and validating models for healthcare underwriting use cases
• Mastery of industry-standard risk scores and large-scale claims data is mandatory
• Expert proficiency in Python and distributed computing (PySpark/Spark/SQL)
• Proficiency in leveraging AI-assisted coding tools (e.g., Claude Code, Cursor, Codex) to accelerate development cycles and enhance code quality
• Expert-level understanding of model evaluation metrics (e.g., Gini, lift, calibration), back testing frameworks, and validation protocols for high-stakes predictive modeling
• Demonstrated ability to lead cross-functional projects, influence technical roadmaps, and solve high-ambiguity problems in high-dimensional datasets
• Exceptional ability to distill complex technical strategies for executive stakeholders and external partners
• Mastery of transformer architectures, attention mechanisms, and pre-training/fine-tuning paradigms
• Hands-on experience with PyTorch or TensorFlow is preferred