Data Scientist Manager

Clicklease•West Valley City, UT

15h

About The Position

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

Requirements

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 LightGBM) 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

Nice To Haves

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

Responsibilities

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