(USA) Data Scientist III

Walmart•Sunnyvale, CA

5d•Onsite

About The Position

Walmart is seeking a Data Scientist III to advance data-driven decision-making by applying advanced analytical modeling, statistical methods, and machine learning techniques. This role involves developing, validating, and deploying scalable models that unlock business value from complex data sets. The successful candidate will collaborate across functions to translate business challenges into data solutions, ensuring data quality and governance standards are met. They will also mentor junior team members and support continuous improvement initiatives, contributing to Walmart’s commitment to operational excellence and innovation through data insights. The Applied AI team develops advanced Search and Personalization solutions to enhance the shopping experience by making it more relevant and intuitive. Leveraging machine learning, deep learning, and generative AI, the team builds scalable systems for efficient product discovery. Team members collaborate closely, applying expertise in algorithms and solution design to deliver impactful results aligned with business goals. The team maintains a professional environment focused on technical excellence, continuous improvement, and customer-centric innovation, driving meaningful advancements in data science and artificial intelligence.

Requirements

Proven expertise in data management, data quality standards, and governance to ensure reliable and scalable data ecosystems.
Strong proficiency in SQL and programming languages such as Python or R for advanced data analysis and model development.
Experience applying statistical methods, machine learning algorithms, and predictive modeling techniques to solve complex business problems.
Ability to conduct exploratory data analysis, feature engineering, and model validation using appropriate optimization and testing methods.
Familiarity with big data analytics platforms and tools, including AWS SageMaker, to support model deployment and scalability.
Solid understanding of business requirements and domain knowledge to translate data insights into actionable recommendations.
Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 2 years' experience in an analytics or related field.
Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field.
4 years' experience in an analytics or related field.

Nice To Haves

Data science, machine learning, optimization models
Master’s degree 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)
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.
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.

Responsibilities

Apply advanced statistical and machine learning techniques to develop predictive models and extract actionable insights from complex data sets.
Conduct exploratory data analysis, feature selection, and hypothesis testing to support data-driven decision-making.
Collaborate with cross-functional teams to translate business requirements into analytical solutions and scalable model deployments.
Ensure data quality and governance standards are maintained throughout the data lifecycle.
Develop, test, and optimize code using programming languages such as SQL and Python to support analytical workflows.
Communicate findings through effective data visualization and reporting to influence business strategies.
Mentor junior team members on modeling and analytical best practices.