Associate Data Scientist, New College Grad - 2026

Visa•Foster City, CA

1d•Hybrid

About The Position

The Global Data Science team at Visa leverages our rich data - spanning over 3 billion card accounts and 100 billion transactions per year- and other third-party data sources to solve meaningful business problems. We are seeking an Associate Data Scientist to support the data science efforts for Visa’s Global Consumer Payments Data and Analytics team. The role is part of the Global Data Office and will work closely with senior data scientists and business partners in day-to-day operations, as well as supports strategic initiatives prioritized by Visa’s Chief Data Officer. This associate will contribute to executing an analytics agenda that delivers insights and AI-powered solutions to improve Visa’s products and services.

Requirements

Bachelor's or Master's degree in Computer Science, Computer Engineering, CIS/MIS, Cybersecurity, Statistics, Business or a related field, graduating May 2025 - August 2026.
Permanent Authorization to work in the U.S. is a precondition of employment for this position.
Visa will not sponsor applicants for work visas in connection with this position.
Future sponsorship will not be considered.

Nice To Haves

2+ years of experience in data analysis, quantitative modeling, or data driven decision making in an academic or professional setting.
Proficiency in SQL and Python for data analysis and modeling.
Experience extracting, transforming, aggregating, and analyzing large datasets using SQL, Python, R, and Spark, including exploratory data analysis and feature engineering.
Hands on experience using Generative AI or AI-assisted tools (e.g., LLMs, coding assistants, AutoML) to support data analysis, insight generation, or workflow efficiency.
Applied experience with Generative AI techniques, such as prompt engineering, text summarization, classification, or LLM assisted analysis, through coursework, projects, or professional work.
Familiarity with responsible AI considerations, including data privacy, bias awareness, and model limitations.
Solid foundation in statistics and machine learning, including regression, classification, and model evaluation techniques.
Hands on experience building descriptive and predictive models using machine learning libraries and tools such as scikit learn, Jupyter notebooks, Python, R, and/or SAS.
Exposure to data mining and statistical modeling techniques, including regression, clustering, decision trees, and related methods.
Experience in building and maintaining BI solutions using tools like Tableau, Power BI, or similar platforms, including metrics definitions, semantic layers, data quality validation, and user support.
Effective communication and collaboration skills, with the ability to explain data driven insights clearly to business stakeholders and translate analysis into actionable recommendations for technical and non-technical audiences.
Experience organizing and managing analytical work using productivity tools such as Excel, Word, PowerPoint, and collaboration platforms (e.g., Teams).
Exposure to financial services, payments, credit cards, or merchant analytics is a plus but not required.

Responsibilities

Contribute to the development and deployment of analytics and machine learning models, supporting use cases from data exploration through validation and implementation under guidance from senior team members.
Apply Generative AI techniques (e.g., prompt engineering, LLM-based text analysis, summarization, and classification) to enhance data analysis, insight generation, and internal workflows.
Leverage largescale datasets using tools such as SQL, Python, R, or Hive, combining traditional statistical methods with ML and GenAI-assisted approaches to uncover trends and actionable insights.
Use AI-powered development tools (e.g., coding assistants, AutoML, and notebook automation) to accelerate experimentation, improve code quality, and increase productivity.
Build and maintain BI dashboards and reports, and support user adoption through documentation, walkthroughs, and guidance on best practices for BI usage.
Develop intuitive visualizations and dashboards to communicate insights and model outputs to technical and non-technical audiences.
Support model deployment and monitoring efforts, collaborating with data engineering teams and following established MLOps, data governance, and responsible AI guidelines.
Partner with product, marketing, operations, and finance teams to understand business questions and translate them into analytical tasks.
Assist in framing business problems into analytical approaches, contributing to data-driven solutions that inform product and operational decisions.
Present insights and recommendations using structured storytelling, clearly explaining assumptions, limitations, and potential business impact.
Support prioritization of analytics initiatives by considering business value, data availability, and technical feasibility in collaboration with senior team members.
Communicate technical findings in simple, actionable terms to nontechnical partners and stakeholders.
Help drive adoption of analytics solutions by validating results, documenting methodologies, and demonstrating how insights address real business needs.
Collaborate closely with cross-functional teams to iterate on analyses and improve solutions based on feedback.