Data Scientist Manager

About the role

The Data Science Manager owns the development, deployment, and lifecycle management of Clicklease’s credit, fraud, and portfolio models, translating business problems into production-ready modeling solutions that drive measurable risk and financial outcomes.

• Lead end-to-end development and lifecycle management of credit, fraud, and portfolio models, including PD, LGD, CNL/CGL forecasts, BAV cash flow scoring, fraud/identity scoring, and collections/recovery models
• Serve as hands‑on technical lead on the most complex and highest‑impact modeling projects, setting standards for experimental rigor, feature engineering, validation methodology, and documentation
• Manage, mentor, and develop the Data Science team, including performance management, coaching, hiring, and prioritization
• Own model governance across the portfolio, including documentation, validation artifacts, backtesting, challenger frameworks, and drift monitoring
• Partner with Data Engineering to design and maintain feature store architecture, training/serving pipelines, and data quality standards
• Translate business questions from Credit Risk, Collections, Finance, Operations, and Sales into well‑scoped modeling projects and executive‑ready recommendations
• Drive evaluation and adoption of new data sources and modeling techniques to improve decisioning quality
• Ensure compliance with fair lending, ECOA/FCRA, adverse action, and internal model risk standards

• Design, develop, validate, and deploy predictive models that directly impact credit, fraud, and portfolio performance
• Lead and maintain model governance practices, including monitoring, backtesting, and compliance validation
• Manage and develop team members, including hiring, coaching, and performance evaluation
• Translate complex analytical outputs into actionable business recommendations for senior stakeholders
• Ensure adherence to regulatory requirements, including fair lending and adverse action compliance
• Design and evaluate experimentation frameworks (A/B/C/D tests, pricing tests, strategy rollouts) with proper statistical rigor and causal inference
• Represent the Data Science function in executive and cross‑functional forums, translating technical outcomes into business impact
• 7+ years of experience in data science, machine learning, or quantitative modeling
• 2+ years of experience leading projects or mentoring data scientists
• Experience building and deploying production models in a regulated financial services environment
• Experience using Python (pandas, scikit‑learn, XGBoost or Light
  
  GBM) for model development
• Experience writing and optimizing SQL queries for analytical workflows
• Experience with full model lifecycle including feature engineering, validation, deployment, and monitoring
• Experience presenting analytical findings and recommendations to cross‑functional stakeholders
• Bachelor’s degree in a quantitative field or equivalent practical experience

• Experience in consumer, small business, or specialty finance lending
• Familiarity with ECOA, FCRA, Reg B, and fair lending requirements
• Experience with MLOps tooling, feature stores, or model monitoring systems
• Experience with advanced modeling techniques such as survival analysis or causal inference
• Exposure to modern ML tooling or LLM‑assisted workflows

• Credit and risk modeling expertise
• Model governance and regulatory compliance
• Cross‑functional stakeholder alignment
• Team leadership and development
• Experimental design and causal inference

• Python (pandas, scikit‑learn, XGBoost/Light
  
  GBM, stats models)
• SQL (Snowflake preferred)
• Machine learning model development and validation
• Data pipeline and feature engineering concepts