Data Scientist

Position Summary 

The Data Scientist is responsible for applying advanced analytics, statistical modeling, and machine learning techniques to support strategic decision-making across the organization. This role partners closely with stakeholders in Underwriting, Actuarial, Claims, Product, Revenue, and Customer Experience to translate business questions into analytical solutions that improve operational performance, risk evaluation, and customer outcomes. 

The Data Scientist develops predictive models, explores new data sources, and generates actionable insights that support pricing, underwriting, claims management, and operational efficiency. This role also contributes to the development and operationalization of models and analytics solutions by working with engineering and product teams to integrate insights into business tools, dashboards, and workflows. 

Essential Duties and Responsibilities  

• Partner with stakeholders across Underwriting, Actuarial, Claims, Product, and Revenue to identify opportunities where data can inform better business decisions. 

• Translate business questions into analytical frameworks, hypotheses, and statistical approaches. 

• Analyze customer, broker, and operational data to identify trends, anomalies, and opportunities to improve engagement, retention, and operational performance. 

• Conduct exploratory data analysis to investigate business problems and generate actionable insights. 

• Communicate analytical findings through visualizations, dashboards, and written summaries. 

• Develop predictive models and statistical analyses to support pricing, underwriting, claims, reserving, and operational initiatives. 

• Apply machine learning techniques including regression, classification, clustering, and other statistical learning methods to solve complex business problems. 

• Evaluate model performance and continuously improve models based on new data and changing business needs. 

• Explore internal and third-party data sources to enhance model performance and uncover new insights. 

• Collaborate with engineering and product teams to deploy analytical models into production systems and operational workflows. 

• Support the implementation of MLOps practices, including model versioning, deployment pipelines, monitoring, and lifecycle management. 

• Monitor models for performance degradation, bias, or drift and implement improvements as needed. 

• Document model assumptions, methodology, and validation processes to support transparency and governance. 

• Contribute to the improvement of internal data infrastructure, ETL pipelines, and reporting architecture. 

• Support the development and optimization of dashboards and analytics tools used by business teams. 

• Ensure data quality, consistency, and reliability across analytical workflows. 

• Identify opportunities to automate recurring analyses and reporting processes. 

• Work closely with business stakeholders to ensure analytics solutions align with operational needs and strategic objectives. 

• Partner with product and engineering teams to integrate analytical capabilities into internal platforms and decision tools. 

• Provide analytical support for ad-hoc requests and strategic initiatives. 

• Other duties as assigned. 

Qualifications 

Education & Experience