Staff Machine Learning Scientist- Generative Ai

Job Description
About the Team:
Visa Research is an organization that focuses exclusively on the scientific foundations of existing, emerging and future commerce-related technologies. With the establishment of a formal research organization and the associated long-term commitment to technology research, Visa joins the ranks of a small number of industry leaders with both the insight and ability to substantially influence and systemically impact the future.
The Staff ML Scientist will work with a team to conduct world-class research on data analytics and contribute to the long-term research agenda in large-scale data analytics and machine learning, as well as deliver innovative technologies and insights to Visa's strategic products and business. This role represents an exciting opportunity to make key contributions to Visa's strategic vision as a world-leading data-driven company. The successful candidate must have strong academic track record and demonstrate excellent software engineering skills. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and excellent collaboration skills.
Essential Functions:
Formulate business problems as technical data problems while ensuring key business drivers are captured in collaboration product stakeholders.
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 needed data transformations on structured and un-structured data.
Build and experiment with modeling and scoring algorithms. This includes development of custom algorithms as well as use of packaged tools based on machine learning, data mining and statistical techniques.
Devise and implement methods for adaptive learning with controls on effectiveness, methods for explaining model decisions where necessary, model validation, A/B testing of models.
Devise and implement methods for efficiently monitoring model effectiveness and performance in production.
Devise and implement methods for automation of all parts of the predictive pipeline to minimize labor in development and production.
Contribute to development and adoption of shared predictive analytics infrastructure
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.