Data Scientist, Credit Risk Analytics

Role Overview

As a Data Scientist within the Credit Risk Analytics vertical, you will be a core contributor leveraging machine learning expertise to build and deploy predictive models that directly impact business value and shape credit risk strategy.

Responsibilities

• Build industry-leading machine learning models for managing credit and fraud risks.
• Collaborate with engineering to deploy models into production environments.
• Leverage complex data sources like credit bureau reports to develop credit and fraud strategies.
• Analyze ad-hoc portfolio performance and conduct root-cause analysis to identify performance drivers.
• Monitor credit risk models in production and extract actionable insights.
• Assess the validity of new machine learning algorithms and alternative data features.

Requirements

• 2-3+ years of experience in fintech or finance applying statistical and machine learning techniques.
• Advanced degree (M.S./Ph.D.) in Statistics, Computer Science, Engineering, or a related technical field.
• Expert knowledge of Python and SQL.
• Solid understanding of coding best practices, model documentation, and ML ops principles.
• Experience in consumer lending (unsecured personal loans or credit cards) is a plus.

Skills

• Python
• SQL
• Machine Learning
• Credit Risk Modeling
• Statistical Analysis