Design, develop and deploy advanced statistical or machine learning models for credit risk, pricing, collections, fraud, and other high-impact business use cases that drive better data-driven decisions
Lead end-to-end delivery of data science initiatives from problem framing and model design through deployment, monitoring and ongoing maintenance
Partner with cross-functional teams including Portfolio Strategy, Engineering, Product, Underwriting, Sales and Collections to integrate models into our applications, and proactively identify and solve problems in critical business areas
Define and set standards for model development, code quality, and documentation; guide technical design decisions across the team
Act as a technical mentor to team members, fostering a culture of continuous learning and rigorous analytical standards
Communicate complex technical concepts and business implications to both technical and non-technical stakeholders
Build and maintain production machine learning pipelines and monitoring systems to ensure models are reliable, scalable and continuously improving

8+ years of hands-on model development and deployment experience using advanced statistical and machine learning techniques such as generalized linear models, gradient boosting and deep learning
Deep experience in building and deploying credit risk models, especially underwriting models, in the fintech, lending or financial services industry is highly preferred.
Experience with real-time models, decisioning engines, and production-grade machine learning pipelines is preferred.
Expert in Python, SQL and Git
Experience with workflow orchestration tools, such as Metaflow is preferred
Experience deploying and managing models within a cloud platform (AWS, Sagemaker)
Strong foundation in statistics and machine learning, and knowledge of experimental design
Excellent project management and communication skills
Strong critical thinking and problem-solving ability
Nice to have experience: cloud data warehouses (e.g. Snowflake, Databricks), Arize, Metaflow, Sagemaker, decision engines (e.g. Taktile), feature stores (e.g. Tecton)
Bachelor's degree in Financial/Applied Math, Operations Research, Economics, and/or Statistics. Masters/PhD is a plus.

Lead Data Scientist

Key skills