Job Description
Job Description
What youll do : Evaluate compromised and merchant testing activity for account risk. Develop / maintain compromised account and card profiles Determine strategies for Block Reissue and Monitoring Reporting and dashboards showing risk progression by member merchant and segment.
Partner with data BI teams to enrich member risk profiles Align with Risk operations for de-risking execution Partner with colleagues throughout the organization to identify process improvement opportunities Contribute to the evolution of our data and decision infrastructure to improve efficiency and effectiveness of portfolio de-risking Establish operational workflow and RPO enhancements Streamline case flows and handoffs with the use of automation for activities including account locks account closures card reissue member messaging and response triggered actionsMinimum Basic Requirement : 2 years of analytics experience BS / Microsoft in Math Statistics Data Science Computer Science Natural Sciences or a related quantitative discipline 2 years of experience with SQL 1 years of experience with statistical analysis Experience with data visualization tools such as Looker / Tableau0
Strong problem-solver communicator and collaborator able to identify opportunities for growth or improvement to advance the goals of both our members and our business Ability to independently thrive in a fast-paced dynamic and often ambiguous work environment Fast learner
Preferred Qualifications : Debit card and account risk management domain experience Ability to quickly develop an understanding of large complex datasets Fintech financial services consulting or a similar strategic or analytical experience Relevant work experience in credit risk and / or financial fraud risk with an understanding of payment systems money movement products and banking finance credit bureau and fraud detection data Work experience with public cloud platforms especially GCP Apache airflow or Git Experience in MLOps infrastructure and tooling including building efficient and reusable data pipelines Knowledge of Python
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