Credit & Data Science Leader

I am currently working with a hyper-growth fintech looking to a hands-on Head of Credit with deep experience in consumer credit risk modeling, including acquisition, pricing, and portfolio strategies. Strong technical fluency in Python, XGBoost, and Scikit-learn is essential, along with a track record of building and deploying decisioning models for credit cards, HELOCs, or auto lending. In this role, you'll play a critical part in shaping the company's credit risk strategy and building the next generation of predictive models that drive portfolio performance and support product expansion. This is a high-visibility leadership role with significant influence across the business, working alongside top talent from leading technology and financial institutions.

What You'll Do:

Credit Modeling & Risk Management

• Design, build, and deploy predictive credit risk models to assess creditworthiness and forecast default risk.
• Continuously improve model performance (e.g., AUC, ROC, KS) and minimize credit losses across a growing portfolio.
• Monitor post-deployment model performance and recalibrate as needed to ensure real-world effectiveness.

Strategic Impact

• Define and evolve the company's credit risk strategy across multiple asset classes.
• Serve as the go-to expert for complex credit challenges and modeling innovation.
• Drive the adoption of machine learning and automation in underwriting and portfolio optimization.

Leadership & Collaboration

• Mentor junior team members and establish best practices in credit modeling and risk analytics.
• Collaborate cross-functionally with engineering, data architecture, DevOps, and product teams to ensure seamless model integration.
• Partner with external stakeholders (e.g., regulators, capital providers) to ensure compliance and transparency.

Qualifications:

• A seasoned credit professional with 10+ years of experience in consumer credit risk or modeling, ideally within a major bank, fintech, or neobank environment.
• Demonstrated ability to define and drive credit strategy, with a history of influencing portfolio performance and credit outcomes at scale.
• Strong hands-on skills in Python and SQL, with deep familiarity using XGBoost and Scikit-learn for model development.
• Solid grounding in statistical analysis, experimental design, and machine learning methodologies, with the ability to apply them to real-world credit problems.
• Proven success in building and deploying production-grade credit models, particularly those that have delivered measurable improvements in key metrics like AUC, ROC, and KS.
• Comfortable operating across the full credit lifecycle - from individual loan-level decisions to strategic regulatory and capital markets engagement.
• Holds an advanced degree (Master's or Ph.D.) in a quantitative field such as Computer Science, Mathematics, Electrical Engineering, or a related discipline from a top-tier academic institution.