Description

Key Responsibilities:

• Own end-to-end delivery and measurable outcomes for assigned predictive underwriting and pricing workstreams, from problem framing through deployment, monitoring, and continuous improvement, including:
• Frame evidence-based recommendations and workstream trade-offs across model quality, risk, cost, scalability, aligning partners on delivery and measurable outcomes
• Defining and documenting the Analytics-Ready Dataset, data-quality, and reusable feature requirements needed for assigned workstreams across claims, pharmacy, utilization, financial, underwriting, and external data
• Applying rigorous point-in-time development and out-of-time validation approaches for claims maturity, seasonality, leakage, stability, and uncertainty
• Developing, comparing, and challenging predictive models, distributions, and hybrid rule/model approaches based on evidence and operating constraints
• Balance near-term delivery with disciplined exploration of emerging methods, including deep learning or GenAI solutions, that can materially improve accuracy, scalability, decision quality, or operating efficiency
• Translating model needs into detailed feature requirements; partnering with business teams to identify additional signals and with Legal to secure approvals
• Managing third-party model evaluations and ROI analyses when external expertise or independent validation is needed
• Producing explainable, reproducible model outputs and following shared documentation, testing, monitoring, retraining, and rollback standards while recommending improvements based on workstream experience
• Champion reuse, standardization, and componentization of data science assets so successful features, pipelines, evaluation patterns, and scoring capabilities can scale across solutions/products
• Translate model outputs into decision support that improves underwriting accuracy, efficiency, adoption, and user experience; use measured business results to recommend whether to scale, iterate, or stop
• Ensure assigned solutions operate within shared evaluation, monitoring, model-risk, responsible-AI, privacy, and regulatory expectations
• Contribute to team capability through hands-on technical reviews, reusable components, mentoring, and pragmatic evaluation of new statistical, machine-learning, and AI methods

Key Characteristics:

• An independent workstream owner who combines strong scientific judgment with hands-on delivery
• A strategic problem-solver who connects modeling decisions to underwriting outcomes and measurable economic value
• An influential collaborator who aligns Underwriting, Business, Product, and Engineering without relying on formal authority
• An effective communicator who explains technical trade-offs, risks, and business impact in a way that enables confident decisions across technical and business audiences
• A collaborative builder who contributes reusable components, documentation, and practical improvements to shared standards

Required Skills & Qualifications:

• Bachelor's or master's degree in Statistics, Data Science, Computer Science, Mathematics, Engineering, or a related quantitative field; an advanced degree is a plus
• 8+ years in data science, machine learning, statistics, actuarial analytics, including substantial work with healthcare, pharmacy, insurance risk, or sensitive longitudinal data and a proven record of independently owning models or analytical workstreams end to end
• Advanced Python and SQL, with experience in scikit-learn, XGBoost, GBMs, or comparable frameworks. Experience using PySpark or comparable distributed-computing tools to work with structured and unstructured data at scale is preferred; PyTorch experience is a plus
• Deep expertise in Supervised and Unsupervised Machine Learning methods, explain ability, statistical distributions, rare-event and high-cost modeling, calibration, and optimization
• Proven ownership of production models and the MLOps lifecycle on AWS or a comparable cloud platform, including Git-based version control, testing, deployment, monitoring, retraining, rollback, documentation, and responsible AI/model-governance practices.
• Familiarity with Kedro or a similar pipeline framework is a plus. Experience with LLMs, prompt engineering, RAG, embeddings, vector DB, or agentic frameworks is helpful but not required.
• Strong business acumen and judgment, with the ability to connect analytical outputs to measurable business outcomes, influence cross-functional decisions, and navigate trade-offs among model quality, risk, cost, scalability, and time to value

Key Skills

Education

Bachelor's or Master's degrees