Staff Data Scientist

Walmart

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Bentonville, AR
Salary: $110,000–$220,000 / yr
Employment: Full-time
Posted: 5 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $188k

This role $165k

$96k $239k

below market most similar roles pay here above market

This role pays more than 50% of similar roles. Most pay $162,000–$214,500 — the blue band above. At the midpoint, this role pays about $165k versus about $188k 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 825 open roles on FindRole.

Listed pay typically runs $121,000–$242,000 across 473 roles with salary data.

Most-posted roles

View all roles at Walmart

At a glance

TL;DR · Staff Data Scientist

Role Posting Log in to save

The (USA) Staff, Data Scientist joins the Finance Technology team to build intelligent solutions for Accounts Payable, Post Payment Audit, and Cerebro. This role involves designing and deploying production-scale machine learning and agentic AI solutions to identify financial anomalies and automate decision-making. You will develop supervised and unsupervised models for classification, clustering, and prediction while building LLM applications featuring RAG, tool calling, and multi-agent workflows. Key responsibilities include creating root cause analysis techniques, establishing evaluation approaches for groundedness and safety, and partnering with engineering teams to productionize models. Required skills include advanced Python, SQL, and experience with modern ML frameworks, vector databases, and MLOps practices. You will solve complex business problems involving invoices, claims, payments, and audit data to transform large-scale financial information into actionable intelligence.

What does a Data Scientist earn?

Median $173200 from 338 postings across 66 companies.

See salary data

Skills

Python SQL Machine Learning Generative AI LLM RAG LangGraph scikit learn TensorFlow Spark Scala R Vector Databases MLOps Anomaly Detection Statistical Modeling Feature Engineering

What you'll do

Design, build, and deploy production-scale ML and AI solutions for complex financial problems like invoices and audit data.
Develop and evaluate supervised and unsupervised models for anomaly detection, classification, clustering, and ranking.
Build root cause analysis and explainability techniques for financial anomalies and audit findings.
Create LLM applications, RAG experiences, and reliable agentic workflows using single and multi-agent systems.
Develop evaluation approaches for ML and agentic AI systems to measure performance, groundedness, and safety.
Partner with engineering teams to productionize, monitor, retrain, and maintain ML and AI solutions.
Contribute to technical design, reusable implementation patterns, and documentation for AI solutions.
Communicate analytical findings and model behavior clearly to both technical and business stakeholders.

What we're looking for

Option 1: Bachelor's degree in a quantitative field and 4 years of experience in an analytics related field.
Option 2: Master's degree in a quantitative field and 2 years of experience in an analytics related field.
Option 3: 6 years of experience in an analytics or related field.
Strong hands-on experience in data science, machine learning, statistical modeling, and applied AI.
Experience building and deploying production ML solutions including anomaly detection, classification, clustering, and feature engineering.
Experience with LLMs, RAG, vector databases, and agentic workflows using frameworks like LangGraph or Google ADK.
Advanced Python and SQL skills with experience using modern ML frameworks and large-scale transactional or financial datasets.
Familiarity with MLOps practices, cloud-based ML platforms, and production deployment patterns.

Save