[Hiring] Staff Data Scientist @Prealize Health

Role Description

As a Staff Data Scientist and the primary SME for Healthcare Financial Risk & Underwriting, you will serve as the technical authority for our most critical Healthcare financial risk and underwriting modeling domains. You will bridge the gap between complex actuarial needs and cutting-edge machine learning, defining the technical roadmap to scale predictive models for risk adjustment and underwriting.

This is a strategic, high-impact role where you will lead the evolution of our predictive models, shaping how we evaluate and power the next generation of our underwriting product.

Key Responsibilities

• Domain Expertise: 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.
• Strategic Vision: Set the technical roadmap and lead the development of ML models focused on healthcare financial risk stratification and financial underwriting.
• Methodological Excellence: 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.
• Technical Leadership: Mentor junior data scientists, fostering a culture of rigorous research, high-quality engineering, and innovation.
• Cross-Functional Collaboration: Partner with clinicians to translate medical knowledge into data-driven hypotheses and with Product/Executive teams to align technical capabilities with market opportunities.
• Platform Innovation: Work with Engineering to design the next generation of our platform’s quality and extensibility, ensuring it remains performant for millions of patients.
• External Evangelism: Represent Prealize Health’s technical expertise to external strategic partners and stay at the forefront of literature in ML, Actuarial Science, and Biostatistics.

Qualifications

• Education: PhD and/or MS in Statistics, Biostatistics, Economics, Computer Science, or a related quantitative field.
• Experience: 8–10+ years of experience building and deploying commercial-grade data science products with a proven track record of technical leadership.
• Domain Expertise: Deep experience building and validating models for healthcare underwriting use cases. Mastery of industry-standard risk scores and large-scale claims data is mandatory.
• Programming & AI Tooling: 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.
• Statistical Rigor: Expert-level understanding of model evaluation metrics (e.g., Gini, lift, calibration), back testing frameworks, and validation protocols for high-stakes predictive modeling.
• Strategic Mindset: Demonstrated ability to lead cross-functional projects, influence technical roadmaps, and solve high-ambiguity problems in high-dimensional datasets.
• Communication: Exceptional ability to distill complex technical strategies for executive stakeholders and external partners.

Preferred Skills

• Deep Learning Expertise: Mastery of transformer architectures, attention mechanisms, and pre-training/fine-tuning paradigms. Hands-on experience with PyTorch or TensorFlow is preferred.

Benefits

• Flexible work environment
• Competitive base salary plus a generous bonus and equity plan
• Paid time off including holidays
• Medical, dental, vision
• 401k
• Wellness and home office benefits, and more

Pay Transparency

The target salary range is $160,000 to $190,000 annually. Base pay offered may vary within the posted range based on several factors, including but not limited to education, job-related knowledge, skills, experience, and location.

Diversity, Equity & Inclusion

Prealize embraces diversity and equal opportunity in a serious way. We are committed to building a team that unites a variety of backgrounds, perspectives, and skills. The more inclusive we are, the greater our impact will be.