Staff ML Scientist - New 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.

Job Description

Job Summary

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.

Key Responsibilities

• 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.

Qualifications

Basic Qualifications

• PhD in Computer Science, Computer Engineering, CIS/MIS, Cybersecurity, Machine Learning, Data Systems or related filed), graduating May 2025 - August 2026

Preferred Qualifications

• 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 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 communications skills, specifically, the absence of repeated grammatical or typographical errors, clear and concise written and spoken communications, and communications that demonstrate professional judgment.
• Relevant coursework in modeling techniques such as logistic regression, Naïve Bayes, SVM, decision trees, or neural networks.
• Deep learning experience with TensorFlow or PyTorch.
• Experience working with large datasets using tools like Hadoop, MapReduce, Kafka, Flink or Hive is a plus.
• The ability to take on challenges and address problems hands-on
• Strong ability to collaborate
• Highly driven, resourceful and results oriented
• Good team player and excellent interpersonal skills
• Good analytical and problem-solving skills
• Demonstrated ability to lead and navigate through ambiguity

This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager. 

U.S. Applicants Only

The estimated salary range for this position is $157,000 to $170,000 USD per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity.Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401(k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program.

Work Hours

Varies upon the needs of the department.

Travel Requirements

This position requires travel 5-10% of the time.

Mental/Physical Requirements

This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers.

Visa is an EEO Employer

Qualified applicants will receive consideration for employment without regard to race, color religion, sex, national origin, sexual orientation, gender identity, disability or protect veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with the EEOC guidelines and applicable local law.