Sr. Credit Risk ML Scientist

As a Senior Credit Risk AI/ML Scientist you will play a huge role not only with building models across our lending portfolio but also be involved in deploying and monitoring of the models in production.

What you will do:

• Develop and implement credit risk scoring models for personal loan origination using traditional and alternate datasets, including cash flow.
• Analyze diverse datasets to identify trends and patterns that may impact credit risk incorporating structured and unstructured data sources.
• Collaborate with cross-functional teams including data engineers, business analysts, and stakeholders to gather requirements and ensure alignment with business objectives.
• Conduct thorough model validation and performance monitoring to ensure accuracy and effectiveness across multiple data streams.
• Stay current with industry best practices, emerging trends, and regulatory requirements related to credit risk modeling, particularly in alternative data usage.
• Communicate findings and insights to technical and non-technical stakeholders in a clear and concise manner, emphasizing the incorporation of alternative datasets.
• Support model deployment and provide ongoing support for model maintenance and enhancements, particularly in the context of evolving data sources and regulatory landscapes.
• Use ML to segment our customers based on behavior and propensity to engage with our various lending products
• Recommend optimal segmentation strategy to build different models for different consumer segments.
• Work on a modern AI/ML platform.
• Use every dataset at disposal to build a feature store for AI/ML models as well as a customer 360 profile.
• Set up testing infrastructure to test different underwriting and credit strategies for different lending products.
• Conceive, design & monitor risk management strategies to improve business profitability in the areas of underwriting, credit line management, universe expansion and alternative data evaluation for lending.

Requirements

Skills and Experience:

• 5+ years consumer credit risk experience including building credit scorecards/scoring systems.
• Hands on experience building credit underwriting models and feature engineering.
• Familiarity with bureau data and alternate data is a strong requirement.
• Strong development experience with Python is a must along with ML libraries (pandas, NumPy, scikit learn, TensorFlow)
• Familiarity with H2O, Azure ML and Amazon Services is a plus but not required.
• Strong Experience in SQL
• Ability to thrive in a fast paced, team-oriented environment.
• Master’s degree or PhD. (preferred) in a quantitative field such as Mathematics, Statistics, Computer Science, Operations Research, Engineering
• Experience in credit scoring models in consumer personal loans in banks and fintech’s preferred.