Senior Applied Scientist

Role overview As a Senior Applied Scientist on a loan-servicing team, you’ll develop predictive and causal models to improve outcomes for borrowers and investors after loans are issued. You’ll turn analysis into practical decisions across repayment support, collections, payment experiences, and borrower communications, working with product, engineering, and operations partners.

Responsibilities - Build and assess models that predict repayment behavior and other servicing outcomes. - Use causal inference and experiments to measure the effects of interventions such as payment assistance and loan modifications. - Convert predictions and estimated treatment effects into decision rules that teams can apply. - Work with product and engineering on payment plans, autopay, and borrower communications, and with operations on collections strategy and agent performance. - Find opportunities to apply machine learning to servicing workflows that have relied on manual approaches. - Explain analytical methods, tradeoffs, and findings to technical and nontechnical partners.

Requirements - Graduate degree in a quantitative discipline such as statistics, mathematics, econometrics, physics, operations research, or computer science. - At least three years of machine learning, statistical modeling, or applied quantitative research experience in academia or industry. - Experience using causal inference, experimentation, statistics, probability, and experimental design to evaluate models or interventions. - Production-quality Python skills, including data manipulation and algorithm implementation. - Ability to frame broad business problems as research questions and communicate findings as actionable recommendations.

Nice to have - PhD in a quantitative field. - Experience with payment products, causal machine learning, or treatment-effect models. - Background in supervised learning, feature engineering, and evaluating models on large or complex datasets. - Experience taking work from exploratory analysis and experiments through production while maintaining analytical rigor and model reliability.

Benefits and work setup - Remote role within the United States, with periodic in-person team meetings or onsite sessions. - Compensation includes base pay, bonus opportunities, and equity; the source lists an anticipated base salary range of $167,700–$231,800 USD. - Benefits include health coverage, retirement contributions, paid time off and family leave, wellness and productivity allowances, and employee support resources.