Citi is currently transitioning its Fraud Analytics Customer Experience team into a data science-based organization. We are looking for an energetic and motivated analytics professional to join our growing team, supporting fraud strategy development for our Branded Credit Card Business with a focus on improving the fraud authorizations customer experience. The candidate will be closely involved with analysis, presentation, implementation, and monitoring of the strategies in the fraud detection system in addition to using analysis to support process improvements outside of authorization strategies all while balancing fraud risk. The candidate will work closely with various stakeholders including fraud analytics, fraud operations, fraud policy, technology, other risk teams, and business owners to coordinate customer experience efforts.
Develop and implement effective fraud authorization suppression strategies to mitigate false positives from fraud loss mitigation efforts for the branded credit card business while ensuring an appropriate balance between risk, operational cost, and customer experience.
Analyze customer transaction data using various techniques such as decision trees, regression, text mining, clustering, or other advanced techniques.
Develop actionable strategies leveraging a diverse set of inputs including POS methods, notifications, customer profiles, status, merchant history, etc.
Identify patterns to detect false positive trends by evaluating combinations or sequence of events from different data sources
Develop and review MIS reports and communicate the results to the business
Partner in root cause analysis and decision quality/defect reviews to identify strategy opportunities to improve performance
Support large scale projects including testing and deployment of new vendor scores or tools, as well as development of business cases for new technology
Work within a business end to end agile framework where teams are cross-functional and empowered, activities are time-boxed around specific outcomes, work is iterative and incremental, and problems are solved in a modular and adaptive manner
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Bachelor’s degree in Statistics, Economics, Finance, Mathematics, Computer Science, or similar quantitative field, is required. An advanced degree is highly desirable.
A minimum of 5 years of experience in applied analytics is required in the banking / financial industry (required), preferably within the fraud risk discipline
Must have strong analytical skills and familiar with large dataset environments (such as UNIX).
Experience in statistical and data analysis in at least one of the following statistical software packages or languages: SAS (required), SQL, R, or Python.
Experience in implementing strategies into real-time fraud prevention systems is desired
Must be extremely motivated with a desire to continuously learn and refine our processes
Excellent organization skills with an ability to be flexible and work in a rapidly changing environment
Excellent communication and presentation skills are required
People and project leadership experience
Experience in the agile methodology of iterative project management, preferred
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