Company Description Visa is a world leader in digital payments, facilitating more than 215 billion payments transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable and secure payments network, enabling individuals, businesses and economies to thrive.
When you join Visa, you join a culture of purpose and belonging - where your growth is priority, your identity is embraced, and the work you do matters. We believe that economies that include everyone everywhere, uplift everyone everywhere. Your work will have a direct impact on billions of people around the world - helping unlock financial access to enable the future of money movement.
Join Visa: A Network Working for Everyone. Job Description Join Visa's Value Added Services organization as a Data Science Intern on the Risk & Security Services team. You'll work alongside experienced ML engineers, data scientists, and risk analysts to help build ML-powered systems that detect fraud, verify identities, and reduce friction for legitimate users at global scale.
This internship is designed to provide hands-on exposure to real-world fraud detection, anomaly detection, and AI-assisted risk investigation systems, with mentorship and structured learning throughout the program.
All Visa roles require digital fluency , including the ability to work with emerging technologies such as Generative AI tools (e.g. ChatGPT, Microsoft Copilot) to support everyday work.
What You'll Do As an intern, you will contribute to scoped, well-defined projects while learning industry best practices:
- Collaborate with product managers, risk analysts, and engineers to understand fraud and identity use cases and translate them into data and modeling tasks.
- Support the development of AI-assisted workflows that help triage risk alerts, enrich signals, or recommend next actions.
- Contribute to LLM or RAG-backed components that summarize evidence or assist analysts in investigations, following clear guardrails and review processes.
- Write clean, testable Python and SQL code for batch or streaming data jobs under mentorship.
- Help monitor model performance and data quality, and assist with dashboards, metrics, or experiments to evaluate impact.
- Learn and apply privacy, security, and responsible AI practices when working with sensitive financial data.
- Document designs, experiments, and findings; share progress in team meetings or demos.
- Participate in code reviews, sprint ceremonies, and team stand-ups, with support from senior engineers.
What You'll Gain - Hands-on experience building ML systems used in real-time financial risk decisioning.
- Mentorship from senior ML engineers and data scientists.
- Exposure to production ML, model monitoring, and governance in a regulated environment.
- Experience working in a large-scale, global engineering organization.
- A strong foundation for future roles in ML engineering, applied data science, or risk analytics.
Qualifications Basic Qualifications - Students pursuing a Bachelor's degree in Computer Science, Computer Engineering, Data Science, CIS/MIS, Cybersecurity, Business or a related field, graduating December 2026 - August 2027.
- Strong communications skills, specifically, the absence of repeated grammatical or typographical errors, clear and concise written and spoken communications that demonstrate professional judgment.
Preferred Qualifications - Proficiency in Python and basic SQL.
- Strong understanding of data structures, algorithms, and software engineering fundamentals.
- Coursework or academic projects in machine learning, statistics, or data analysis.
- Familiarity with at least one ML framework such as scikit-learn, PyTorch, or TensorFlow.
- Experience using Git or another version control system.
- Academic or personal projects related to fraud detection, security analytics, identity systems, or risk modeling.
- Exposure to time-series data, graph features, or streaming systems (e.g., Spark, Kafka) through coursework or projects.
- Familiarity with common anomaly detection or sequence models:
- Isolation Forest, LOF, autoencoders
- HMMs, RNNs, or Transformer-based models (introductory level)
- Basic understanding of LLMs and Retrieval-Augmented Generation (RAG) concepts.
- Curiosity about AI agents, tool-using workflows, or decision automation in real-world systems.
Additional Information U.S. APPLICANTS ONLY: The estimated hourly range for a new hire into this position is $35-$40/hr 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 Authorization: Visa will not sponsor applicants for work visas in connection with this position. Future sponsorship will not be considered.
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 protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.
Visa will consider for employment qualified applicants with criminal histories in a manner consistent with applicable local law, including the requirements of Article 49 of the San Francisco Police Code
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