Staff Machine Learning Scientist

Visa U.S.A. Inc., a Visa Inc. company, needs a Staff Machine Learning Scientist (multiple openings) in Austin, TX to:

• 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.
• Develop AI/Machine learning models to detect credit card information attacks.
• Formulate business problems as technical data problems while ensuring key business drivers are captured in collaboration with 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.
• Develop custom algorithms and use 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, and 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.
• Position reports to the Austin, TX office and may allow for partial telecommuting.

Requirements

Basic Qualifications:

• Employer will accept a Ph.D. degree in Computer Science, Machine Learning, Statistics, Engineering or related field.
• Machine learning
• Deep learning
• Python programming
• Advanced statistics
• Scala programming
• Spark programming
• Graph learning
• Functional data analysis