Lead Data Scientist

Visa•Bellevue, WA

2d•Hybrid

About The Position

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. We are looking for a versatile, curious, and highly technical Lead Data Scientist to shape the future of AI and data products across the Visa Acceptance Platform. You will partner with engineers, product leaders, and AI researchers to design, deploy, and scale advanced machine learning solutions across a global ecosystem. In this role, you will: Define technical strategy for AI/ML models supporting global payments and next-generation agentic commerce experiences. Mentor a high-performing team and establish best-in-class practices for data science, MLOps, and model governance. Lead end‑to-end development of large-scale data solutions—from data extraction through modeling, deployment, and monitoring. Drive platform transformation initiatives that enhance delivery efficiency, improve model performance, and enable new product opportunities. What You Will Do (Essential Functions) Provide technical leadership in designing scalable, maintainable, and secure data science solutions; set standards for modeling, coding patterns, and MLOps. Lead efforts to improve data extraction, data quality, lineage, and governance, partnering with engineering teams across the platform. Develop strategies for wrangling, modeling, and leveraging high-volume structured and unstructured data using modern AI/ML techniques. Guide conversations with Product, Cybersecurity, and Engineering to clarify complex business and technical requirements and ensure secure deployment. Identify emerging patterns across massive datasets to influence platform‑wide enhancements, roadmap decisions, and AI-driven product improvements. Drive modernization opportunities including software upgrades, security patches, and infrastructure improvements. This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.

Requirements

10+ years of relevant work experience with a Bachelor’s Degree or at least 7 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 4 years of work experience with a PhD, OR 13+ years of relevant work experience.

Nice To Haves

12+ years of relevant work experience with a Bachelor’s Degree in Computer Science or Data Science or at least 7 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 4 years of work experience with a PhD, OR 13+ years of relevant work experience.
3+ years building and deploying AI/ML models for predictive analytics or insights.
4+ years working with large-scale data technologies such as Hadoop, Hive, Kafka, Spark, Redis, NoSQL, or RDBMS.
2+ years designing and maintaining ETL or data pipelines.
Experience working with Big Data, distributed computing, and streaming/real-time systems.
Strong experience in metrics design, experimentation, and model evaluation.

Responsibilities

Define technical strategy for AI/ML models supporting global payments and next-generation agentic commerce experiences.
Mentor a high-performing team and establish best-in-class practices for data science, MLOps, and model governance.
Lead end‑to-end development of large-scale data solutions—from data extraction through modeling, deployment, and monitoring.
Drive platform transformation initiatives that enhance delivery efficiency, improve model performance, and enable new product opportunities.
Provide technical leadership in designing scalable, maintainable, and secure data science solutions; set standards for modeling, coding patterns, and MLOps.
Lead efforts to improve data extraction, data quality, lineage, and governance, partnering with engineering teams across the platform.
Develop strategies for wrangling, modeling, and leveraging high-volume structured and unstructured data using modern AI/ML techniques.
Guide conversations with Product, Cybersecurity, and Engineering to clarify complex business and technical requirements and ensure secure deployment.
Identify emerging patterns across massive datasets to influence platform‑wide enhancements, roadmap decisions, and AI-driven product improvements.
Drive modernization opportunities including software upgrades, security patches, and infrastructure improvements.