Senior Applied Data Scientist, Clair

• 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

Desirable

• As an Applied Data Scientist on Clair’s Data Science team, you’ll be responsible for developing and maintaining the next generation of credit models and predictive systems that power Clair’s financial products
• You’ll shape how millions of hourly workers access affordable credit, leveraging cutting-edge data science to assess risk, predict repayment, and optimize credit decisions
• We’re looking for someone who thrives at the intersection of data science, machine learning, and fintech, and who’s excited to translate messy, real-world data into clear, actionable intelligence
• This role blends credit modeling with feature engineering — from designing new model features (including predictive features) based on user transaction data to deploying and monitoring production-ready ML models that drive critical product decisions
• To succeed, you’ll need a deep technical foundation in machine learning and statistical modeling, hands-on experience in model deployment, and strong communication skills to collaborate with engineers, product managers, and risk teams
• 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