Manager, Data Science – Credit & Fraud Risk Modeling

This is a fully remote position, open to applicants in New York.

📋 Description

• Take ownership of the complete lifecycle of machine learning models that drive credit risk decisions.

• Design, develop, implement, and oversee models that assess customer approvals, credit limits, default predictions, and loss forecasts.

• Extract insights from both internal and external datasets to create high-signal features like DTI, PTI, payment behavior, and account balance trends.

• Create strategic credit risk models that encompass approval amount sensitivity, credit line optimization, and loss forecasting models.

• Collect, cleanse, and transform financial data into datasets that are primed for modeling.

• Assess third-party data vendors and scoring products; conduct cost-benefit analyses and determine potential integrations.

• Implement state-of-the-art machine learning techniques derived from research to tackle production-level credit risk challenges.

• Collaborate with engineering teams on model deployment, validation, and ensuring repeatable processes.

• Spearhead model recalibration and redevelopment when performance indicators show signs of drift.

• Manage model risk governance, adhere to regulatory standards, and comply with data vendor usage policies.

• Convert business inquiries from risk, finance, and sales into modeling challenges and communicate solutions in accessible language.

• Deliver presentations to executives and influence credit policy formulation.

⛳️ Requirements

• Master's or PhD in a quantitative field such as Statistics, Mathematics, Data Science, Econometrics, or a related area.

• At least 5 years of experience as a Data Scientist or ML Engineer, emphasizing predictive modeling.

• Ideally, a background in credit risk, fraud detection, or financial analytics.

• Proven experience in deploying models that impact actual credit or lending decisions.

• Proficient in advanced Python for statistical modeling and machine learning.

• Strong SQL skills for data extraction and feature engineering.

• Extensive knowledge of structured/tabular data machine learning algorithms, including gradient boosting, ensemble methods, regression models, decision trees, and AutoML frameworks.

• Previous experience in consumer lending, fintech, or financial services is highly desirable.

• Practical experience with model risk governance frameworks and collaboration with validation teams.

• Familiarity with SR 11-7 guidelines.

• Capability to articulate technical models to risk committees and convey credit policy trade-offs to engineers.

• Direct experience in credit risk modeling.

🏝️ Benefits

• Coverage of 80% for medical, dental, and vision insurance costs, inclusive of spouse, children, and other dependents.

• 401k retirement plan.

• Flexible paid time off starting from day one.

• Options for remote work flexibility.

• Competitive salary package.