Lead Data Scientist

Responsibilities

• Identify and frame high-value problems across functional areas
• Translate business questions into analytical strategies, experiments, and measurable outcomes
• Develop, test, and deploy predictive models that drive profitable growth and improve operational performance across the enterprise
• Apply modern ML and AI techniques to accelerate development cycles, improve model performance, and deliver new capabilities
• Build production-ready solutions: robust data pipelines, feature engineering, measurement discipline (KPIs, guardrails, and experiment design), model monitoring, and clear, reproducible documentation aligned to best practices
• Communicate with impact: tell the story with data, present recommendations to technical and non-technical stakeholders, and influence decisions at senior levels
• Advance team excellence: evaluate new methods and tools, share reusable components, elevate engineering standards, and (at Senior/Lead) mentor others and help shape technical direction

Qualifications

• PhD in a quantitative field (PhD strongly preferred)
• Strong foundation in statistics and applied modeling—you can connect theory to practical, business-relevant solutions
• Strong hands-on experience with modern modeling tools and methods, including: Python (strongly preferred) and/or R for statistical modeling, SQL for large-scale data transformation and analysis, GLMs and tree-based methods/GBMs (e.g., H2O, XGBoost, LightGBM); familiarity with clustering, Bayesian methods, regularization, and optimization is a plus
• Experience with AI (e.g., NLP/LLMs, deep learning, computer vision) applied to feature generation, model development, and business process improvement is helpful but not required
• Able to deliver results in real-world settings: structured problem-solving, experimental mindset, and pragmatic decision-making
• Senior candidates must have a proven track record of end-to-end model ownership including shipping models into production, and improving them through monitoring, measurement, and iteration
• Strong communication skills—able to present and explain methods, assumptions, tradeoffs, and results clearly
• Experience working with cloud and modern data platforms (especially AWS: S3, EC2, SageMaker; and Snowflake)
• Strong grasp of relational databases and experience working with large, multi-source datasets
• Comfort working in Git-based, version-controlled environments; strong documentation practices are required
• Insurance industry experience is helpful but not required

Pay

The pay range for this position is $152,000 to $217,000 annually. Actual compensation will vary based on multiple factors, including employee knowledge and experience, role scope, business needs, geographical location, and internal equity.