Staff Data Scientist

Role overview

Lead risk data science for consumer lending, developing models and decision systems that help balance loan growth with portfolio performance. The role spans credit risk, fraud, collections, and loss forecasting, with ownership from research and validation through production deployment. You will advise senior stakeholders and help raise technical and governance standards across the data science team.

Responsibilities

- Develop and operate models for credit scoring, default prediction, fraud detection, and collections. - Design, backtest, and validate risk models and loss forecasts using statistical methods and machine learning. - Lead cross-functional modeling projects from initial analysis through production release, coordinating scope and timelines. - Explain model behavior, portfolio projections, and analytical findings to senior and non-technical audiences. - Guide other data scientists and establish sound modeling, documentation, and governance practices. - Work with risk, pricing, product, capital markets, engineering, finance, and compliance teams on decision systems and production monitoring.

Requirements

- At least 7 years in quantitative risk modeling, credit or fraud analytics, or applied data science; 8+ years using Python or R and SQL. - A master’s or doctoral degree in a quantitative field such as statistics, economics, computer science, or operations research. - Direct technical ownership of underwriting or fraud models, ideally in consumer lending, fintech, or financial services. - Strong knowledge of credit risk methods, including PD/LGD/EAD, survival analysis, logistic regression, gradient-boosted trees, and risk-adjusted returns. - Experience with model lifecycle management, validation, backtesting, monitoring, and delivery of complex projects. - Ability to mentor colleagues and communicate technical work clearly to senior stakeholders.

Nice to have

- Familiarity with consumer credit rules and fair lending, cloud platforms such as AWS, GCP, or Azure, and tools such as Looker, Airflow, or dbt. - Experience using alternative data sources.

Benefits and work setup

The role includes an annual performance bonus and equity, alongside health, dental, and vision coverage; retirement savings with employer match; tuition support; paid time off and parental leave; and home office, internet, and phone support.