Job Duration: Long-Term  Job Description:

• Program Overview: He is in the Mode and Infrastructure Team within Credit Risk to develop and move risk models into production risk platform to utilize data and deployment of models. Build and manage a team of ~20 Data Scientists, Quants and Data Analysts to develop, implement and transform Capital One Credit Risk Models and Loss Forecasting System for Retail Portfolio (Mortgage/Card/Auto) through advanced analytics and innovations to enhance the enterprise's decision making;
• Credit Risk Modeling and Implementation for Retail Portfolios (Auto, Card & Mortgage);
• Loss Forecasting, ACL, CECL;
• CCAR and Stress Testing;
• Cash Flow Engine;
• Model Monitoring and Model Calibration/Adjustment;

Required Skills:

• Must Have: Python programming, AWS, Spark, Machine modeling
• Nice to have: SQL, snowflake, tableau
• Good understanding of Credit Risk models not viewed but working very closely with the model teams
• Data Scientist and engineering team deploying models care about inefficiencies need to streamline the process instead of create solutions
• Leverage tools and tech for data visualization for monitoring and performance dashboard development data visualization would be key
• Mostly working utilizing Python, Pyspark, AWS and Flask have models written in Spark version of R moving from R to Python
• Python experience is key
• AWS experience is required
• Spark is a must have
• Expecting 3-5 years of experience rates need to reflect the levels of experience
• Data Scientist who knows modeling and Python connect the models, deploy the models and optimize them financial engineer
• BAU monitoring in Python - understanding models
• Help to be able to convert or optimize modeling code or exclusion into a more efficient way converting spark in to Task and Pyspark converting and optimizing code
• Converting and intent must be correct, testing is key.
• Previous FinTech experience is a nice to have

Tech stack:

• Python
• Spark
• Machine Learning modeling
• Credit Risk Modeling
• Predictive Modeling