Senior, Data Scientist | Fraud & Risk Team | Sunnyvale

Walmart•Sunnyvale, CA

1d•Onsite

About The Position

Walmart is seeking a hands-on, full-stack Senior Data Scientist for its Fraud & Risk team within the Data & AI organization. The role focuses on using machine learning, deep learning, anomaly detection, and large-scale data to detect fraud, prevent abuse, and protect customer interactions across Walmart’s commerce ecosystem. The position works with large-scale online and offline commerce data and collaborates with Data Scientists, Engineers, Product Managers, and business stakeholders to develop and deploy scalable AI-driven solutions with measurable impact. This is a full-time onsite role based in Sunnyvale, California. Remote and hybrid options are not available. Immigration sponsorship is not available; applicants must be authorized to work in the United States for any employer without current or future visa sponsorship.

Requirements

Ph.D. in Computer Science, Data Science, Statistics, Machine Learning, or a related quantitative field, plus at least 2 years of industry experience; or a master’s degree in a related quantitative discipline plus at least 5 years of relevant machine-learning experience.
Proficiency with machine-learning and data-mining packages in Python.
Experience working with large datasets and distributed-computing tools.
Programming skills in Java or JavaScript on a UNIX or Linux platform.
Experience deploying solutions in production.
Must be authorized to work for any employer in the United States without current or future visa sponsorship.

Nice To Haves

Experience with GCP or BigQuery.
Experience with GenAI or Agentic AI.
Preferred qualifications include data science, machine learning, or optimization-model experience; a master’s degree in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, or Econometrics; successful completion of one or more assessments in Python, Spark, Scala, or R; and experience using open-source frameworks such as scikit-learn, TensorFlow, or Torch.
Knowledge of creating inclusive digital experiences and implementing WCAG 2.2 AA standards, assistive technologies, and digital accessibility best practices.

Responsibilities

Design, build, and implement predictive models using high-dimensional, fast-moving real-time datasets.
Own fraud and abuse risks for a product or product segment and partner strategically with product development teams.
Share insights and recommendations through effective communication and data storytelling.
Apply machine learning, AI, deep learning, and data mining techniques to develop robust anomaly-detection models, such as bot detection.
Build data visualizations and interactive dashboards to monitor and share business insights internally and externally.
Pursue research and U.S. patent-filing opportunities to address new fraud issues, develop tools, or automate processes.
Deploy machine-learning solutions in production.