Visa has the world’s largest consumer payment transaction dataset.  We see data on over 250 billion transactions every year from all over the world. We use that data to help our clients in the payment ecosystem grow their businesses and to help consumers access a fast, safe, and rewarding payment experience. Visa Predictive Modeling (VPM) team develops and maintains predictive machine learning models to primarily support Visa Risk and Identity Solutions. Using VisaNet data and leveraging Machine Learning (ML) and Artificial Intelligence (AI), our model scores help Visa clients all over the world for fraud defense, identity verification, smart marketing, etc. Through our models and services, VPM fuels the growth of Visa clients, generates, and diversifies revenues for VISA, while improving Visa Card customer experience and their financial lives.Within VPM, the Acceptance Risk Model Team is responsible for developing real-time fraud detection models serving merchants. We leverage a set of rich data available at merchant check-out including transactional, digital and identity information to detect and stop fraud.This is a Technical (Individual Contributor) role.  Your responsibilities include:Building and validating predictive models with advanced machine learning techniques and tools to drive business value, interpreting, and presenting modeling and analytical results to non-technical audience.Conducting research using latest and emerging modeling technologies and tools (e.g., Deep Neural Networks, RNN, LSTM, etc.) to solve new fraud detection business problems.Improving the modeling process through MLOps and automation to drive efficiency and effectiveness.Partnering with a cross functional team of Product Managers, Data Engineers, Software Engineers, and Platform Engineers to deploy models and/or model innovations into production.Managing model risks in line with Visa Model Risk Management requirements.Conducting modeling analysis to address internal and external clients’ questions and requests.This is a hybrid position. Hybrid employees can alternate time between both remote and office. Employees in hybrid roles are expected to work from the office 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs.
