Principal Data Scientist

The Principal Data Scientist is a highly experienced individual contributor who applies advanced analytics and machine learning to complex P&C insurance problems, including underwriting, pricing, and risk selection. They own the end-to-end analytical lifecycle and collaborate with Actuarial, MLOps, and IT teams to deliver scalable, production-ready solutions.

Location: United States

Salary: $137,900–$231,000

Responsibilities

• Acquires, organizes, and cleanses structured and unstructured data.
• Conducts in-depth analysis to uncover trends, risks, and business opportunities.
• Applies statistical modeling, machine learning, and advanced analytics to develop predictive and prescriptive solutions.
• Evaluate solution performance using statistically rigorous methods and measure the impact to business outcomes.
• Collaborate with MLOps and IT partners to transition solution prototypes from pilot validation into production environments.
• Ensures ongoing model health through post‑deployment monitoring, drift detection, and audit‑compliant governance practices.
• Creates and communicates results to senior level audiences of varying backgrounds, using business-facing presentations, reports, and dashboards.
• Author and maintain comprehensive technical documentation for data lineage, codebases, results, and production changes.
• Provides technical and project guidance, including peer review of work, for data science team.
• Leads the evaluation of new analytic tools and processes.
• Drives investigation and adoption of advanced machine learning and AI innovations.

Requirements

• Bachelor’s Degree in Data Science, Statistics, Mathematics, Operations Research, Actuarial Science, Computer Science, Engineering, Physics or related technical field. Advanced degree preferred.
• 10 years of experience in data science or related advanced analytics domains, including research and teaching, with 3+ years of technical leadership.
• Broad experience supporting underwriting functions within multi-line commercial P&C insurance settings, including 3+ years of loss modeling for General Liability or Commercial Property.
• Demonstrated expertise using Poisson, Gamma, and Tweedie distributions to build loss ratio, pure premium, and frequency–severity loss models for pricing.
• Extensive experience leveraging supervised learning models (e.g., XGBoost, GLM, etc.) and unsupervised techniques (e.g., K-means clustering) to solve complex data science problems.
• Advanced Python programming skills, including scikit-learn, and proficient ETL abilities using SQL.
• Comfortable explaining machine learning models with partial dependence plots and SHAP values.
• Ability to conduct experiments e.g., A/B Testing, to evaluate the causal impact of model-driven decisions.
• Experience using version control tools such as Git and Azure DevOps.
• Experience working in cloud computing environments such as Azure, AWS, GCP, etc.
• Experience developing Agentic AI solutions to enable autonomous decision‑making and task orchestration.