Principal Responsibilities:

📍Work entails heavy focus on developing and implementing best-in-class risk analytic solutions, inclusive of scoring and non-scoring models. Create and deliver powerful insights from data through better visualization and storyboarding 📍Work with a broader team that consists of Business Managers, Consultants and Data Scientists from both Visa and client organizations to strategize, co-create, deploy and reap the benefits of data-driven solutions 📍Work with regional and global Data Science teams to develop high-quality analytic products and solutions that promote Visa’s growth in the region 📍Keep Visa at the forefront of technological advancement in Data Science by introducing cutting-edge tools and techniques for generating business insights

Professional Experience:

📍Minimum of 6+ years of expertise in applying Machine Learning solutions to business problems – model development and production experience required 📍Post-graduate degree (Masters or PhD) in quantitative fields such as Statistics, Mathematics, Data Science, Operational Research, Computer Science, Informatics, Economics, or Engineering 📍Excellent knowledge, experience and understanding of quantitative techniques (modelling, statistics, root-cause, etc.) applied to Risk Management with a focus on Card and Payments. Familiarity with key Risk and Performance Indicators. Experience working in one or more of the Card & Payments markets around the globe, with specific responsibilities in payments, retail banking, or retail merchant industries 📍Good understanding of Payments and the Banking industry, including card verticals such as consumer credit, consumer debit, prepaid, small business, commercial and co-branded product 📍Expert knowledge of data, market intelligence, business intelligence, and AI-driven tools and technologies, with demonstrated ability to incorporate new techniques to solve business problems 📍Experience planning, organizing, and managing multiple large projects with diverse cross-functional teams, including resource planning and delivery implementation

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