ML Scientist - College Grad

About Us

Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid. At Visa, you'll have the opportunity to create impact at scale—tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world.

Join Visa and do work that matters – to you, to your community, and to the world. Progress starts with you.

Visa is rapidly growing our Value-Added Services product portfolio across the globe, and the VAS Platform & Engineering organization is at the intersection of the numerous technologies, platforms and solutions enabling this growth. VAS Innovations team drives execution on our innovation, generative AI, and platform modernization efforts. This team is active in complex, multi-business stakeholder initiatives where innovative integration patterns are required, and emerging technologies are applied to enhance functionality and deliver more value to the market.

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.

• Formulate business problems as technical data problems, ensuring key business drivers are captured in collaboration with product stakeholders.
• Work with product engineering teams to ensure implementability of solutions.
• Deliver prototypes and production code based on business needs.
• Experiment with in‑house and third‑party data sets to test hypotheses on relevance and value of data to business problems.
• Build data transformations for structured and unstructured data.
• Build and experiment with modeling and scoring algorithms, including development of custom algorithms and use of packaged tools based on machine learning, data mining, and statistical techniques.
• Devise and implement methods for adaptive learning with controls on effectiveness, explainability of model decisions, model validation, and A/B testing.
• Monitor and maintain model effectiveness and performance in production environments.
• Automate all parts of the predictive pipeline to minimize labor in development and production.
• Contribute to the development and adoption of shared predictive analytics infrastructure.

• PhD in Computer Science, Computer Engineering, CIS/MIS, Cybersecurity, Machine Learning, Data Systems or related field, graduating May 2025 – August 2026.

• Publications or presentation in recognized Machine Learning and Data Mining journals/conferences is a plus.
• Experiences with one or more of the below topics:
  Natural Language Processing, Knowledge Graph, Time Series analysis, Generative AI, Large Language Model, Meta Learning, Reinforcement Learning, Image Processing.
• Ability to program in one or more scripting languages such as Perl or Python and one or more programming languages such as Scala, Java, C++ or C#.
• Experience with one or more common statistical tools such as SAS, R, KNIME, Matlab.
• Excellent understanding of algorithms and data structures.
• Excellent analytic and problem‑solving capability combined with ambition to solve real‑world problems.
• Excellent verbal and written communication skills.
• Strong communication skills, specifically the absence of repeated grammatical or typographical errors, clear and concise written and spoken communications, and communications that demonstrate professional judgment.
• Relevant…