This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. 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 $180,000 to $220,000 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.

• Lead the design, development, and deployment of advanced machine learning and generative AI models aimed at improving the efficiency and effectiveness of customer onboarding. • Apply a strong understanding of machine learning fundamentals, various frameworks, and model selection to solve complex business problems. • Stay up-to-date with the latest advancements in data science and AI, actively exploring and integrating cutting-edge techniques into our solutions. • Utilize your robust statistical knowledge to inform model development, interpret results, and ensure the reliability of our analytical insights. • Demonstrate exceptional and creative problem-solving skills to tackle unique challenges within the ServiceTitan customer journey. • Design, implement, and rigorously test Generative AI and ML solutions, evaluating their performance and impact. • Strategically determine the most appropriate modeling approach (traditional ML vs. Generative AI) for specific problems, and effectively design tests to evaluate multiple approaches. • Collaborate closely with engineering and product teams to seamlessly deploy models into production environments, ensuring scalability and performance. • Leverage cloud computing services, preferably Azure, to build and maintain robust data science infrastructure and pipelines.

• Partner with product and engineering teams to translate business requirements into data-driven solutions and analytical frameworks • Design and implement advanced statistical methodologies and predictive models to enhance data analysis capabilities • Deliver clear actionable insights through effective data visualization and comprehensive reporting to key stakeholders • Drive operational efficiency by developing and deploying analytics solutions that provide meaningful business intelligence • Lead data quality initiatives and establish data governance standards • Architect and optimize data pipelines for scalable analytics solutions • Develop automated monitoring systems for data integrity and model performance • Maintain expertise in emerging data technologies and industry best practices while continuously improving analytical processes

• Conduct exploratory and diagnostic data analysis (EDA) with a focus on dental insurance plans or similar sectors. • Develop predictive models and recommendation algorithms (e.g., next best offer, segmentation, classification). • Translate complex data into clear insights to support strategic decisions for Product and Business teams. • Identify behavioral patterns, segment customers, and recommend data-driven actions to improve customer experience and retention. • Support continuous product improvement initiatives using evidence derived from data. • Serve in a consultative capacity with stakeholders across different areas, proposing high-impact analytical approaches. • Work with large volumes of structured and unstructured data using modern analysis and modeling tools. • Use platforms such as Python, SQL, and visualization tools (e.g., Power BI) to communicate results.