This role pays more than
54%
of similar roles. Most pay
$136,875–$214,500
— the shaded band above.
At the midpoint, this role pays about
$165k
versus about
$176k
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
184 open roles
on FindRole.
Listed pay typically runs
$117,000–$234,000
across 180 roles with salary data.
(USA) Staff, Data Scientist will join the Marketplace data science team to build machine learning products for an e-commerce platform serving third-party sellers. This role involves owning the full MLOps lifecycle, including data monitoring, refactoring code, and building robust model monitoring workflows. You will develop scalable infrastructure like Kafka pipelines for unified logging, apply SRE principles to optimize cloud deployments, and manage training, inference, and performance monitoring pipelines across various initiatives. The position requires expertise in Python, SQL, Git, and big data platforms including Hadoop, Hive, Pig, Map Reduce, Scala, and Spark. You will also utilize deep learning, supervised and unsupervised learning, and statistical analysis to solve complex problems. Key responsibilities include mentoring junior engineers, conducting code reviews, and collaborating with stakeholders to align technical solutions with business objectives for seller operations like onboarding and fulfillment.
What does a Data Scientist earn?
Median $162025 from 276 postings across 61 companies.
Build scalable end-to-end data science solutions and machine learning products for Walmart Marketplace.
Own the full MLOps lifecycle, including data monitoring, code refactoring, and model management workflows.
Develop and maintain scalable MLOps infrastructure, such as Kafka pipelines for unified logging and monitoring.
Apply Site Reliability Engineering (SRE) principles to optimize cloud deployments and troubleshoot performance issues.
Build distributed training, inference, and performance monitoring pipelines across multiple marketplace initiatives.
Improve data feedback and annotation workflows to increase the efficiency of the labeling team.
Provide architectural guidance, break down complex tasks, and mentor junior machine learning engineers.
Consult with stakeholders to align technical AI solutions with core business objectives.
What we're looking for
Candidates must have a Bachelor's degree plus 4 years of experience, a Master's degree plus 2 years of experience, or 6 years of experience in an analytics field.
Candidates must possess strong knowledge of machine learning foundations, including supervised and unsupervised learning and deep learning.
Candidates must be able to analyze complex problems and translate them into data science algorithms.
Candidates must have experience with big data analytics to identify trends, patterns, and outliers in large volumes of data.
Candidates must have strong experience in Python with excellent knowledge of Data Structures.
Candidates must have strong experience with big data platforms including Hadoop (Hive, Pig, Map Reduce, Scala, Spark).
Candidates must have hands-on experience with Git, SQL, and relational databases or data warehouses.
Candidates should be proficient in MLOps lifecycle management, SRE principles for ML workloads, and building scalable training/inference pipelines.