• The Marketing Analytics Lead builds, maintains, and operationalizes a trusted marketing data foundation that powers reporting, insights, and AI-ready analytics. • Designs and manages scalable ETL/ELT pipelines and analytics-ready data models across the marketing ecosystem—CRM, marketing automation, paid media, web analytics, and messaging platforms—ensuring data is accurate, governed, and accessible for full-funnel measurement. • Designs, develops, and maintains reliable ETL/ELT pipelines that ingest and unify marketing data from multiple systems. • Builds and optimizes analytics data stores and curated, analytics‑ready datasets that support scalable reporting, self‑service analysis, and standardized full‑funnel measurement. • Implements and maintains standardized definitions, keys, and documentation to ensure consistent interpretation, data quality, and downstream usability. • Builds and maintains dashboards and recurring performance reporting that supports marketing leadership, demand generation, and sales alignment—focused on business outcomes. • Partners with the Marketing Analytics Director to deliver insights and recommendations by summarizing trends, identifying drivers, and highlighting opportunities for optimization across channels and funnel stages. • Monitors and troubleshoots pipelines and integrations; proactively identifies failure points, resolves incidents, and improves resiliency through alerting, validation checks, and documented recovery steps. • Maintains detailed runbooks and operational documentation to support continuity, scalability, and onboarding.

• Lead discovery and solution design for GenAI use cases, translating business problems into concrete architectures (LLM decision, RAGs, fine‑tuning, agents, guardrails) • Build end‑to‑end GenAI applications: data ingestion, retrieval layer, orchestration (e.g. LangChain/LlamaIndex/LangGraph), API/backend, and simple UI where needed. • Design and implement RAG pipelines with vector databases, hybrid search, rerankers, query transformation, and evaluation frameworks for relevance and robustness. • Perform model selection, prompting strategies, and fine‑tuning (LoRA/QLoRA/SFT) for text, code, and multimodal models, including evaluation and A/B testing. • Implement safety, compliance, and governance controls (input/output filters, PII handling, audit logs, human‑in‑the‑loop review where required). • Collaborate with data engineers, product owners, and full‑stack developers on scalable architectures, SLAs, and integration with existing enterprise systems • Gather technical requirements and estimate planned work. • Mentor other data scientists/engineers in GenAI patterns, code quality, and best practices; contribute to internal libraries, templates, and reusable components. • Stay current with GenAI landscape (new open and hosted models, agentic frameworks, evaluation techniques) and perform targeted PoCs to validate them.

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.