Associate Data Scientist, New College Grad - 2026  at Visa

About the Role

We are seeking an Associate Data Scientist to support the data science efforts for Visa’s Global Cross-Border team. This role is part of the Global Data Office and will work closely with senior data scientists and business partners in day-to-day operations.

Key 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 large-scale datasets using SQL, Python, R, Hive, or SAS, 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.
• →Business Partnership & Strategy
• →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.
• →Cross-Functional Collaboration
• →Communicate technical findings in simple, actionable terms to non-technical 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.