01Key Responsibilities
Develop acquisition & account management Credit and Fraud model development using Statistical and other Machine Learning techniques like XGBoost, Random Forest, Light GBM etc.Lead the Model Monitoring efforts, work closely with Model Owners, Model Users and Report execution teams to track and understand model performance, deep dives to find root causes for performance deteriorations, and device action plans for the same. Work on Model Documentation and support Model Risk Management team in completing their responsibilities including but not limited to Model validation reviews, Annual Reviews, periodical assessment.Work with Credit & Fraud strategy teams to understand the model usage in strategies, and explain the changes in model performance and suggest any changes that might be required in strategies.Work independently with stakeholders on multiple projects and perform other duties and/or special projects as assigned.Collaborate with cross functional teams, across the globe Model Development, Model Risk Management, Data Infrastructure, as well as Business Provide analytic support on key business initiatives and develop a strong working knowledge of the data warehouse and applicable analytic data systems.Operate as a doer-leader, manage a team, with a focus on prioritizing deliverables / timelines, upskilling talent to latest techniques and technologies, as well as providing cross functional experiences Required Skills/Knowledge:
Bachelor's degree with quantitative underpinning (i.e., Risk, Economics, Finance, Mathematics, Statistics, Engineering) with minimum 5+years' experience in an analytical/quantitative role related to consumer lending or in lieu of a degree 7 + years' of relevant experience in an analytical/quantitative role related to consumer lending.Experience in Python, Pyspark , SAS/SQL and reporting tools like TableauExperience using AWS across the ML lifecycle, including data processing, feature engineering, model development, training, and deployment supportExperience in credit risk and fraud model development end-to-end in areas such as application scorecards, transaction fraud detection models
