(USA) Staff, Data Scientist

Walmart•Sunnyvale, CA

About The Position

The Staff Data Scientist will drive machine learning innovation across Walmart’s BuyBox, Search, and Personalization ecosystem. The role develops next-generation solutions involving ranking, recommendation systems, generative AI, and neural networks, leading initiatives from problem formulation and research through experimentation, deployment, and continuous improvement. The position identifies appropriate data sources, ensures data quality, and builds scalable models supporting customer and business decisions. Solutions operate at Walmart scale across products, offers, sellers, and customer interactions. The role is part of the BuyBox Data Science team within Search & Personalization. The team builds ranking and decisioning systems that help customers discover relevant and competitive products and offers, including selecting the best offer for a product and the most relevant variant from eligible variants. The Staff Data Scientist partners with Product, Engineering, Analytics, and business stakeholders, provides technical leadership, establishes machine learning practices, and mentors Data Scientists.

Requirements

Proven expertise in machine learning, ranking, recommendation systems, information retrieval, and personalization.
Proficiency in Python, BigQuery, PySpark, Hive, and data visualization tools.
Deep understanding of Learning-to-Rank, neural networks, embeddings, representation learning, and modern recommendation techniques.
Experience applying generative AI and large language models to real-world machine learning problems.
Experience building and deploying data pipelines, MLOps, and large-scale production machine learning systems.
Strong knowledge of experimentation, A/B testing, statistical methods, and model evaluation.
Ability to lead complex, ambiguous initiatives and influence technical direction across teams.
Strong communication and cross-functional collaboration skills.
Passion for innovation, applied research, and delivering measurable customer and business impact at scale.
Option 1: Bachelor’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or a related field and 4 years of experience in an analytics-related field.
Option 2: Master’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or a related field and 2 years of experience in an analytics-related field.
Option 3: 6 years of experience in an analytics or related field.

Nice To Haves

Data science, machine learning, and optimization models experience.
Ph.D. 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.
Experience using open-source frameworks such as scikit-learn, TensorFlow, or Torch.
Background creating inclusive digital experiences, including knowledge of WCAG 2.2 AA standards, assistive technologies, digital accessibility best practices, and Walmart accessibility standards and guidelines.

Responsibilities

Lead advanced machine learning solutions for BuyBox offer ranking, Variant Leader Selection, Search, and Personalization.
Build and advance ranking and recommendation systems using Learning-to-Rank, deep learning, and modern machine learning techniques.
Apply generative AI, large language models, and neural networks to ranking, semantic understanding, personalization, and customer experience.
Drive research and innovation, translating new ideas into scalable production solutions.
Lead projects from problem definition and modeling through offline evaluation, A/B testing, production deployment, and impact measurement.
Partner with Product, Engineering, Analytics, and business stakeholders on strategy and technical roadmaps.
Provide technical leadership, establish machine learning best practices, and mentor Data Scientists.
Deploy and monitor production models, ensuring performance and scalability.
Identify appropriate data sources, ensure data quality, and develop scalable models that support customer and business decisions.