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# Data Scientist

- **Company:** Wal-Mart Stores, Inc.
- **Location:** San Jose, CA, United States
- **Experience:** Experienced
- **Salary:** $143,000.0 - $286,000.0
- **Contract:** Permanent contract
- **Skills:** Adobe Analytics, Java (Programming Language), Web Accessibility, Amazon Web Services, Data Analysis, Application Frameworks, Artificial Neural Networks, Microsoft Azure, Big Data, Cloud Computing, Information Retrieval, Python (Programming Language), Logistic Regression, Machine Learning, Tensorflow, Scala (Programming Language), Support Vector Machine, Web Content Accessibility Guidelines, Google Cloud, Pytorch, Large Language Models, Multi-Agent Systems, Random Forest, Apache Spark, Deep Learning, Generative AI, Scikit Learn, Information Technology, Xgboost, Synthesizing Data
- **Published:** October 5, 2026
- **Apply:** https://www.themuse.com/jobs/walmart/usa-staff-data-scientist-af4713

## About the Role

* Experience with Large Language Models (LLMs).
 * 5-7 years of experience as technical on-hands data scientist building and deploying solutions.
 * Solid understanding of Machine Learning models including Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting Decision and Deep Neural Networks.
 * Familiarity with Deep Learning frameworks including Tensorflow and Pytorch.
 * Experience with Cloud technology including GCP, AWS, and Azure.
 * Proficient in Java and Python., Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.

Option 1: Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 4 years' experience in an analytics related field. Option 2: Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 2 years' experience in an analytics related field. Option 3: 6 years' experience in an analytics or related field

Preferred Qualifications...

Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.

Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch), We value candidates with a background in creating inclusive digital experiences, demonstrating knowledge in implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA standards, assistive technologies, and integrating digital accessibility seamlessly. The ideal candidate would have knowledge of accessibility best practices and join us as we continue to create accessible products and services following Walmart's accessibility standards and guidelines for supporting an inclusive culture.

## Description

We are seeking a Staff Data Scientist with a strong foundation in machine learning, information retrieval, and LLMs. The selected candidate will be responsible for architecting and developing new Machine Learning models to enhance Customer Segmentation landscape within Walmart. This is an exciting opportunity for a creative and innovative professional who thrives in a fast-paced, results-driven environment.

What you'll do...

 * Develop generative AI LLM-based applications, , multi-agent workflows, machine learning algorithms and computational algorithms based on business initiatives.
 * Direct the gathering of data, assessing data validity and synthesizing data into large analytics datasets to support project goals.
 * Utilize big data analytics and advanced data science techniques to identify trends, patterns, and discrepancies in data. Determine additional data needed to support insights.
 * Build and train statistical models and machine learning algorithms for replication for future projects.
 * Develop custom models and algorithms to drive business solutions, and scale them to large data sets.