Sr Credit Data Scientist

4 or more years of professional experience in data science, machine learning, and artificial intelligence, with a focus on fraud risk management in underwriting, behavioral surveillance, and loss prevention in the financial services industry.

Master’s or Ph.D. degree in a quantitative field such as Mathematics, Statistics, Data Science, Operations Research, Computer Science.

Strong knowledge of fraud risk-drivers in small and medium sized businesses, public firms, and private firms, including data typically used in fraud risk management from external credit bureaus and internal risk management processes

Advanced knowledge of SQL and experience creating and managing large datasets to organize and extract useful information

Advanced knowledge of Python or R and experience with common data science libraries such as lightgbm, scikit-learn, pandas, etc..

Deep understanding of model deployment requirements for scalable solutions and real-time feature stores

Deep expertise in statistical and machine learning techniques, including modeling, testing and inference, sampling methods, supervised and unsupervised learning.

Strong communication and presentation skills with an ability to relate complex analytics findings to business outcomes

Adaptable and comfortable working collaboratively and independently in a self-starting manner

Evidence of creative problem solving, critical thinking and a continual learning mindset in credit risk management.

Prior experience mentoring experience of data scientists

Prior roles responsible for building underwriting and behavioral models.

Knowledge of external intelligence sources, including third-party data providers e.g. LexisNexis, Socure, Dun and Bradstreet

Experience using cloud environments to develop advanced models, such as AWS Sagemaker

Experience with end–to-end machine learning systems and MLOps framework

AI Tool Experience Preferred -- Gemini, ChatGPT, Similar