Visa Associate Data Scientist, New College Grad - 2026 \\\| SmartRecruiters Google Chrome Microsoft Edge Apple Safari Mozilla Firefox . Associate Data Scientist, New College Grad - 2026 Full-time Job Family Group: Data Science and Analytics Company Description Visa is a world leader in payments and technology, with over 259 billion payments transactions flowing safely between consumers, merchants, financial institutions, and government entities in 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 while driven by a common purpose to uplift everyone, everywhere by being the best way to pay and be paid. Make an impact with a purpose-driven industry leader. Join us today and experience Life at Visa. Job Description The Global Data Science team at Visaleveragesour 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 Scientisttosupportthedata science efforts for Visas Global Consumer Payments Data and Analytics team. Therole ispart of the Global Data Office and willwork closely with senior data scientists and business partnersin day-to-day operations, as well as supports strategic initiatives prioritized by Visas Chief Data Officer.This associatewillcontributetoexecuting ananalytics agenda that delivers insights and AI-powered solutions to improve Visas 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-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 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 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. 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 with cross-functional teams to iterate on analyses and improve solutions based on feedback. Qualifications 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. 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 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. Additional Information U.S. APPLICANTS ONLY: The estimated salary for a new hire into this position is $120,000whichmay 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 forbonusand 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: 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. Work Hours: Varies upon the needs of the department This is a hybrid position: Expectation of days in office will be confirmed by your hiring manager. Travel Requirements: This position requires travel5-10% 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,frequentlyoperatestandard 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 alsoconsider foremploymentqualified 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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Candidates should possess a Bachelor's or Master's degree in Computer Science, Computer Engineering, CIS/MIS, Cybersecurity, Statistics, Business, or a related field, graduating between May 2025 and August 2026. A minimum of two years of experience in data analysis, quantitative modeling, or data-driven decision-making is preferred. Proficiency in SQL and Python for data analysis and modeling is required, along with experience in extracting, transforming, aggregating, and analyzing large datasets. Experience with Generative AI or AI-assisted tools, including prompt engineering and LLM-based analysis, is also sought. A solid foundation in statistics and machine learning, including regression and classification techniques, is necessary.