Develop and maintain credit risk and repayment models that directly power Clair’s underwriting and decision systems.
Design and engineer predictive features using transaction-level data, including the prediction of user income flows, spending, and repayment behaviors.
Collaborate with engineering teams to deploy, monitor, and optimize models in production environments using AWS and Snowflake ML Platform.
Build prototypes of new modeling methodologies and present them to stakeholders for evaluation and feedback.
Investigate and analyze credit performance, identifying root causes of model drift or unexpected repayment behavior.
Partner cross-functionally with product, risk, and engineering teams to turn data insights into product improvements and credit strategy refinements.
Communicate technical concepts clearly to both technical and non-technical audiences.
Stay current on best practices and emerging techniques in credit modeling, explainable AI, and financial ML systems.

5+ years of professional experience in data science and machine learning.
Proven track record of leading end-to-end data science projects — from research and model development to deployment and monitoring.
Experience building and productionizing credit or risk assessment models.
Strong proficiency in Python and familiarity with modern ML libraries (TensorFlow, PyTorch, XGBoost, etc.).
Strong command of SQL and experience working with large transactional and time-series datasets.
Strong communication skills and ability to translate complex findings into actionable insights.
Experience working with cross-functional teams in a fast-paced production environment.

Applied Data Scientist

Key skills