Associate Data Scientist, New College Grad - 2026

We are seeking an Associate Data Scientisttosupportthedata science efforts for Visa's Global Consumer PaymentsData andAnalytics team. Therole ispart of the Global Data Office and willwork closely with senior data scientists and business partnersin day-to-day operations, as wellassupportsstrategic initiatives prioritized by Visa's Chief Data Officer.This associatewillcontributetoexecuting ananalytics agenda that delivers insights and AI-powered solutions to improve Visa's products and services.

Essential Functions:

Hands-On Data Science & Innovation

• 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-basedtext analysis, summarization, and classification) to enhance data analysis, insight generation, and internal workflows.
• Leverage largescale datasets usingtools such asSQL, Python, R,orHive, combining traditional statistical methods with ML andGenAI-assisted approaches to uncover trends and actionable insights.
• UseAI-powereddevelopment tools (e.g., coding assistants,AutoML, and notebook automation) to accelerate experimentation, improve code quality, and increase productivity.
• Build andmaintainBI 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 andmonitoringefforts, collaborating with data engineering teams and following establishedMLOps, data governance, and responsible AI guidelines.

Business Partnership & Strategy

• Partner with product, marketing, operations, and finance teams to understand business questions and translate them into analytical tasks.
• Assistin framing business problems into analytical approaches, contributing todata-drivensolutions 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.

Cross-Functional Collaboration

• Communicate technical findings in simple, actionable terms to nontechnical partners and stakeholders.
• Help drive adoption of analytics solutions byvalidatingresults, documenting methodologies, anddemonstratinghow insights addressreal businessneeds.
• Collaborate closely withcross-functionalteams to iterate on analyses and improve solutions based on feedback.

Basic Qualifications

• Bachelor's orMaster's degree in Computer Science,Computer Engineering, CIS/MIS, Cybersecurity,Statistics,Businessor a related field, graduatingMay2025 - August 2026.

Preferred Qualifications

• 2+ years of experience in data analysis, quantitative modeling, or data driven decision making in an academic or professional setting.
• Proficiencyin 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.
• Handson experience using Generative AI orAI-assistedtools (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 LLMassistedanalysis, 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,Jupyternotebooks, Python, R, and/or SAS.
• Exposure todata mining and statistical modeling techniques, including regression, clustering, decision trees, and related methods.
• Experienceinbuilding andmaintainingBI solutions using tools like Tableau, Power BI, or similar platforms, includingmetricsdefinitions, semanticlayers, data quality validation, and user support.
• Effective communication and collaboration skills, with the ability to explaindata driven insights clearlytobusiness stakeholdersandtranslateanalysis into actionable recommendations for technical andnon-technicalaudiences.
• 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 notrequired.