Senior Applied Scientist – ML for Credit Servicing (Remote)

OhioX; Columbus, Austin, San Francisco (OH, TX, CA); Remoto; USD 168.000 - 232.000; Tempo pieno

Vantaggi offerti da questo lavoro

Competitive compensation

Annual equity grants

401(k) with company match

Medical/dental/vision coverage

Paid time off

Remote-friendly

Descrizione del lavoro

Upstart is hiring a Senior Applied Scientist for the Servicing team. You will build predictive and causal models to improve loan servicing outcomes and translate findings into practical decision rules.

You will partner with Product, Engineering, and Operations to optimize payment experiences, autopay, and borrower communications. The role requires a graduate quantitative degree, strong causal inference, and production-level Python coding, with 2+ years of ML or quantitative research experience.

Competenze

• Graduate degree in mathematics, applied mathematics, statistics, physics, econometrics, operations research, computer science, or a related quantitative field.
• Experience applying causal inference methods and experimentation to real-world decisions in an applied science or data science context.
• Applied statistics, probability, and experimental-design expertise used to build or evaluate models or interventions.
• Production-quality coding experience in Python, including data manipulation and algorithm implementation.

Mansioni

• Develop and evaluate machine-learning models that predict borrower repayment behavior and other servicing outcomes.
• Apply causal inference and experimentation to measure the impact of servicing interventions, including loan modifications and payment assistance.
• Translate model predictions and treatment-effect estimates into practical decision algorithms and actions.
• Partner with Product and Engineering on payment experiences, autopay, and borrower communications, and with Servicing and Collections Operations on collections strategy and agent-performance insights.
• Identify and develop machine-learning opportunities in servicing areas that have historically relied on manual optimization.
• Explain analytical choices, tradeoffs, and findings to technical and non-technical partners to inform high-stakes operational and product decisions.

Conoscenze

Causal inference

Applied statistics

Experiment design

Python coding

Formazione

Graduate degree in a quantitative field

Strumenti

Python

Descrizione del lavoro

Upstart is hiring a Senior Applied Scientist for the Servicing team. You will build predictive and causal models to improve loan servicing outcomes and translate findings into practical decision rules.

You will partner with Product, Engineering, and Operations to optimize payment experiences, autopay, and borrower communications. The role requires a graduate quantitative degree, strong causal inference, and production-level Python coding, with 2+ years of ML or quantitative research experience.