Staff Data Scientist

Visa•Bellevue, WA

10h•Hybrid

About The Position

We are seeking a highly technical and hands-on Staff Data Scientist to contribute to the development of AI-driven data products across the Visa Acceptance Platform. In this role, you will work closely with engineering, product, and architecture partners to design, build, and deploy machine learning solutions that operate at global scale. This is an execution-focused role for an experienced data scientist who thrives in production environments and enjoys solving complex data and modeling problems that directly impact customers.

Requirements

5 or more years of relevant work experience with a Bachelors Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD
3+ years of experience working with large-scale data technologies such as Spark, Kafka, Hadoop, Hive, NoSQL, or relational databases.
2+ years of experience designing and maintaining ETL or data pipelines.
Experience working with big data, distributed systems, and batch or streaming data processing.
Strong understanding of metrics design, experimentation, and model evaluation techniques.
Proficiency in Python and SQL; experience with modern ML frameworks is a plus.

Nice To Haves

6 or more years of work experience with a Bachelors Degree or 4 or more years of relevant experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or up to 3 years of relevant experience with a PhD
2+ years of experience building and deploying AI/ML models for analytics, predictions, or recommendations in production environments.

Responsibilities

Design, develop, and deploy machine learning models and data solutions that support analytics, predictive insights, and AI-powered platform features.
Perform data exploration, feature engineering, modeling, and validation using large, complex datasets.
Build, maintain, and improve data pipelines and model workflows in partnership with engineering teams.
Apply appropriate metrics and evaluation techniques to measure model performance, reliability, and business impact.
Collaborate with Product, Engineering, and Security partners to translate business requirements into scalable and secure data science solutions.
Contribute to platform modernization efforts by improving data quality, performance, reliability, and observability.
Support operational readiness through monitoring, troubleshooting, and continuous improvement of deployed models.