Machine Learning Scientist

The ML Scientist will work with a world-class team at the middle of a global payments revolution focusing on multiple convergent trends across all geographies that Visa operates in. You will conduct extremely high-quality applied AI & ML research and contribute to the long-term research agenda in large-scale machine learning for Visa. This role will make key contributions to Visas strategic vision as a world-leading data-driven company. You will deeply understand the business problem, formulate it as a mathematical problem, identify datasets from which alpha can be derived, and finally provide innovative solutions for Visas strategic products and business. The successful candidate must have strong academic track record, deep understanding of algorithms, hands-on experience on very large sized datasets, and demonstrate excellent software engineering skills. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, excellent collaboration skills, and a commitment to pushing the boundaries of applied sciences.

Essential Functions

• Formulate business problems as technical and mathematical problems, and collaborate with business and product stakeholders to ensure key business drivers are impacted.
• Work with product engineering to ensure implementability of solutions. Deliver prototypes and production code based on need.
• Experiment with in-house and third-party data sets to test hypotheses on relevance and value of data to business problems.
• Build end-to-end ML pipelines across data extraction, feature engineering, training, inference, and validation, using very large structured and unstructured data sources. This includes development of custom algorithms as well as use of packaged tools for machine learning, analytics, and statistical techniques.
• Devise and implement methods for adaptive learning with controls on efficiency, methods for explaining model decisions where vital, model validation, A/B testing of models.
• Devise and implement methods for efficiently monitoring model efficiency and performance in production.
• Contribute to development and adoption of shared predictive analytics infrastructure.
• Use AI copilots to automate and gain efficiency across the complete ML model development lifecycle.