Principal Data Scientist

Walmart

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Bentonville, ARBellevue, WASunnyvale, CA
Salary: $110,000–$220,000 / yr
Employment: Full-time
Posted: 53 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $190k

This role $165k

$95k $249k

below market most similar roles pay here above market

This role pays less than 59% of similar roles. Most pay $165,000–$214,500 — the blue band above. At the midpoint, this role pays about $165k 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.

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At a glance

TL;DR · Principal Data Scientist

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JOB TITLE: (USA) Principal, Data Scientist The Principal Data Scientist joins the Associate AI Experiences team to provide technical leadership and long-term vision for next-generation AI systems. This senior individual contributor will architect end-to-end ML systems, including data pipelines, feature stores, and production deployment for autonomous AI agents. Key responsibilities include designing agentic evaluation frameworks, deploying NLP pipelines, and mentoring senior talent on advanced modeling techniques. The role requires deep expertise in PyTorch, transformer architectures, and ML algorithms like XGBoost and CatBoost. Candidates must demonstrate proficiency with LangChain, RAG patterns, and multi-cloud platforms including AWS SageMaker and GCP Vertex AI. The work focuses on solving high-impact business problems by building intelligent platforms and multi-agent orchestration systems to improve workflows across stores, fulfillment centers, and distribution centers using BERT, FAISS, and time series forecasting.

What does a Data Scientist earn in Washington?

Median $198000 from 42 postings across 16 companies.

See salary data

Skills

Agentic AI Python PyTorch LangChain RAG NLP Computer Vision Time Series Forecasting AWS SageMaker GCP (Vertex AI BigQuery Docker MLOps XGBoost CatBoost LightGBM Random Forest BERT SBERT FAISS Scikit-learn Pandas NumPy Spark Scala R TensorFlow

What you'll do

Architect and deploy end-to-end ML systems including data pipelines, feature stores, and production serving layers.
Build and ship production-grade autonomous AI agents using LangChain, Pydantic AI, and RAG patterns.
Design agentic evaluation frameworks to benchmark performance across task completion and multi-step reasoning.
Deploy enterprise-scale NLP pipelines using BERT/SBERT embeddings and vector search for semantic similarity.
Lead high-impact projects from initial problem framing and prototyping through to production rollout.
Author technical designs and standards while presenting quantified business impacts to senior leadership.
Mentor senior scientists and engineers on advanced modeling techniques, experimentation design, and feature engineering.
Establish best practices for model validation, experimentation, and the safe deployment of AI systems.

What we're looking for

Master's degree in a quantitative field with 3 years of experience, Bachelor's with 5 years, or 7 years of experience in an analytics field.
Deep expertise in ML algorithms (XGBoost, CatBoost, LightGBM, Random Forest, AutoML) and deep learning (PyTorch, transformer architectures).
Production experience with NLP (BERT, SBERT, FAISS, RAG), computer vision (YOLO, CLIP), and time series forecasting (ARIMA, Prophet).
Hands-on experience building and orchestrating multiple AI agents with LangChain, RAG, tool integration, and memory management.
Multi-cloud ML platform proficiency in AWS SageMaker and GCP (Vertex AI, BigQuery, BigQuery ML).
Strong Python skills (Pandas, NumPy, Scikit-learn) with containerization (Docker) and MLOps practices.
Advanced statistical analysis, experiment design, and causal inference skills.
PhD in a related field and experience with optimization models or publications (preferred).

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