Senior Data Scientist

Role overview Lead research that turns complex business data into evidence, models, and product opportunities. You’ll work with senior leaders and engineering partners to identify decisions data science can improve, develop a forward-looking view of the business, and communicate what the analysis can—and cannot—show.

Responsibilities - Set a research agenda, frame hypotheses, and guide investigations from question to evidence and recommendation. - Work with business leaders, customers, and subject matter experts to assess opportunities in areas such as risk, fraud, retention, pricing, and compliance. - Partner with machine learning and data engineers to develop forecasts, leading indicators, and production models. - Explore data, engineer features, and validate models using methods such as holdout testing, leakage checks, calibration, error analysis, and fairness review. - Assess the value of internal and external data, identify gaps, and contribute to data product strategy and pricing. - Explain findings to technical and nontechnical audiences, translate insights into product proposals, and define success measures before launch.

Requirements - Senior, end-to-end data science experience spanning exploration, hypothesis development, feature engineering, modeling, and validation. - Strong statistical judgment, including the ability to detect noise, leakage, and confounding and communicate uncertainty. - Fluency in Python and SQL, plus experience working with engineering teams to put models into production. - Strong business analysis skills and the ability to size opportunities and connect evidence to decisions. - Experience leading research or analytics from initial question through recommendation, such as in strategy, operations, due diligence, or an internal analytics team. - Excellent written and verbal communication, including presenting recommendations to executives and nontechnical audiences.

Nice to have Experience in transportation, logistics, fleet, insurance, safety, or regulated compliance; forecasting or scenario planning for executives; turning analysis into paid products; data sourcing or licensing; graph data, entity resolution, or document and text analysis with language models.

Benefits and work setup Medical, dental, and vision coverage; retirement plan with employer match; paid time off and holidays; wellness programs; and an employee assistance program.