This role pays more than
85%
of similar roles. Most pay
$145,000–$214,500
— the shaded band above.
At the midpoint, this role pays about
$214k
versus about
$180k
for comparable roles.
Based on 240 similar postings.
Employer
About Walmart
Walmart Inc. is the world''s largest retailer by revenue, operating a chain of hypermarkets, discount department stores, and grocery stores, as well as a growing e-commerce presence through Walmart.com. Industry: General Merchandise & Grocery Retail
Walmart currently has
333 open roles
on FindRole.
Listed pay typically runs
$117,000–$234,000
across 268 roles with salary data.
The (USA) Principal, Data Scientist will join the Search and Personalization team to lead the design, development, and deployment of AI-powered systems for global eCommerce platforms. This role involves owning the end-to-end delivery of machine learning systems for search ranking, query understanding, retrieval, and real-time personalization. You will build and productionize deep learning and Transformer-based models, while applying Generative AI, LLMs, RAG, and fine-tuning to improve product discovery. Key responsibilities include designing large-scale feature engineering pipelines, defining experimentation strategies, and mentoring senior scientists. The role requires proficiency in Python, Scala, PyTorch, and Spark to handle petabyte-scale catalog signals. You will solve complex information retrieval and ranking problems to improve the customer journey across various discovery touchpoints.
What does a Data Scientist earn in California?
Median $214500 from 102 postings across 35 companies.
Own the end-to-end design and delivery of ML systems for search ranking, retrieval, and real-time personalization.
Build and productionize deep learning and Transformer-based models for intent classification and semantic matching.
Apply Generative AI and LLMs, including RAG and fine-tuning, to improve product discovery.
Design large-scale feature engineering and training pipelines for petabyte-scale catalog and behavioral signals.
Define experimentation strategies and align offline and online metrics for search and personalization systems.
Drive technical architecture decisions and influence the long-term AI roadmap for global eCommerce platforms.
Mentor senior scientists and establish team standards for model development and production deployment.
What we're looking for
Meet one of three education/experience paths: Bachelor's with 5 years analytics experience, Master's with 3 years analytics experience, or 7 years of analytics experience.
Proven track record in Search, Information Retrieval, Ranking, Recommendations, or Personalization at production scale.
Hands-on experience with PyTorch and Transformer architectures for relevance modeling, semantic search, or multi-task learning.
Practical experience applying Generative AI to real products, including RAG, instruction fine-tuning, or RLHF.
Strong engineering skills in Python and Scala with experience building large-scale Spark-based ML pipelines.
Solid command of experimentation including causal inference, A/B testing, interleaving, and offline/online metric alignment.
Experience operationalizing ML systems at scale, including model serving, drift monitoring, and continuous training.
PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, or publications in related journals (preferred).
Machine Learning
Generative AI
Large Language Models
LLMs
Python
Spark
Scala
R
TensorFlow
scikit learn
MLOps
Vector Search
Information Retrieval
Recommendation Systems
Transformer Models
Embeddings
Distributed Model Deployment
Causal Inference