Senior Credit Data Scientist

About the role

Credit and fraud risk have to be measured and watched over the whole client lifecycle, and that calls for models that hold up in production. The team pairs data scientists with domain experts and stakeholders to get there.

At a glance

What you will do

• Work with stakeholders to turn fraud risk needs into data-driven solutions
• Build and review code and automated pipelines that pull risk signals from large application and transaction datasets
• Apply machine learning and statistical methods to design models that scale and run automatically
• Guide junior data scientists and share practices with them

What they look for

• Four or more years in data science, machine learning or AI, focused on fraud risk in underwriting, behavioral monitoring or loss prevention
• A master's or PhD in a quantitative field such as statistics, mathematics, data science, operations research or computer science
• Advanced SQL and experience managing large datasets
• Advanced Python or R, with libraries such as lightgbm, scikit-learn and pandas
• Understanding of what model deployment needs for real-time, scalable use
• Knowledge of fraud risk drivers for small and medium businesses and for public and private firms, including credit bureau data