below marketmost similar roles pay hereabove market
This role pays more than
87%
of similar roles. Most pay
$165,000–$214,500
— the blue band above.
At the midpoint, this role pays about
$214k
versus about
$190k
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
713 open roles
on FindRole.
Listed pay typically runs
$117,000–$234,000
across 610 roles with salary data.
JOB TITLE: (USA) Principal, Data Scientist
The Principal, Data Scientist joins the One Demand Forecasting organization to advance large-scale demand forecasting and machine learning solutions. This role involves designing and improving models that analyze data across numerous item, store, and location combinations to support demand planning. Key responsibilities include building forecasting models, experimenting with advanced statistical techniques like regression and time series analysis, and collaborating with engineering teams to define feature pipelines and deploy models into production. The candidate will utilize Python, SQL, and frameworks such as Scikit-learn, TensorFlow, or PyTorch within cloud environments like AWS, GCP, or Azure. The work focuses on solving complex forecasting problems by integrating diverse data sources like promotions and macroeconomic indicators to improve accuracy, scalability, and explainability for critical business decisions.
What does a Data Scientist earn in California?
Median $214500 from 132 postings across 33 companies.
Build and improve forecasting models to support demand planning across item, store, and location levels.
Implement advanced ML and statistical techniques like regression, time series analysis, and probabilistic modeling.
Define feature pipelines and improve model training workflows in collaboration with engineering teams.
Prototype new forecasting methods including ensembling models and deep learning architectures.
Integrate diverse data sources such as promotions, events, and macroeconomic indicators into model inputs.
Monitor and evaluate model performance by building dashboards and detecting anomalies using SQL or Python.
Maintain responsible AI practices by contributing to model explainability, fairness, and data governance.
What we're looking for
Option 1: Bachelor's degree in a quantitative field and 5 years of experience in an analytics related field.
Option 2: Master's degree in a quantitative field and 3 years of experience in an analytics related field.
Option 3: 7 years of experience in an analytics or related field.
Foundational experience with machine learning, data analysis, or forecasting through 8+ years of professional experience or relevant academic graduate-level coursework/projects.
Strong programming skills in Python and experience with machine learning libraries like Scikit-learn, TensorFlow, or PyTorch.
Proficiency in SQL and experience working with large datasets or cloud computing environments like AWS, GCP, or Azure.
Experience with containerization (e.g., Docker) or MLOps tools (preferred).
PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, or publications in related journals (preferred).
GenAI
LLMs
Python
Machine Learning
Deep Learning
Spark
Hive
GCP
Azure
CI/CD
TensorFlow
scikit learn
Distributed Systems
ML Pipelines
Natural Language Processing
Scala
R
Python
Machine Learning
Optimization
Statistical Analysis
Deep Learning
AWS SageMaker
GCP
Spark
Scala
R
scikit learn
TensorFlow
Data Visualization
Model Validation
Operations Research