Data Science Intern

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.