Lead Data Scientist - Hybrid

Job Summary: The Lead Data Scientist is responsible for developing and delivering statistical and machine-learning solutions for automated underwriting and related business problems. The role translates complex business needs into rigorous analytical approaches and ensures that solutions are accurate, interpretable, scalable, and practical for business use. This is a hands-on role that also provides technical direction, reviews analytical work, mentors other data scientists, and communicates recommendations to technical and business stakeholders.

Position Responsibilities: Partner with underwriting, business, product, and technology stakeholders to identify and prioritize data science opportunities. Lead data science projects from problem definition and data exploration through modeling, evaluation, implementation, and enhancement. Develop statistical and machine-learning models using techniques appropriate to the business problem, available data, and intended use. Analyze large and complex datasets to identify patterns, generate insights, and support business decisions. Define model-evaluation approaches and ensure solutions are accurate, interpretable, stable, and aligned with business objectives. Evaluate new data sources, analytical methods, and third-party solutions through structured analysis and experimentation. Collaborate with data engineers, machine-learning engineers, software engineers, and technology teams to implement analytical solutions in production. Monitor model performance, investigate unexpected outcomes, and recommend improvements as data and business conditions evolve. Prepare clear technical documentation to support implementation, validation, governance, and ongoing model management. Provide technical direction, review analytical work, and mentor data scientists and other analytical contributors.