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About This Role
Position Summary...
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What you'll do...
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Position Summary...
The Catalog Data Science team at Walmart plays a pivotal role in maintaining and enhancing the data quality of Walmart's massive catalog. We aid supplier onboarding, merchandise acquisition, inventory management, and shopper experience by leveraging cutting\-edge technologies in GenAI, Machine Learning, Deep Learning, and Engineering. We tackle complex problems spanning natural language understanding, image classification, and recommendation to outlier detection, visualization, and model serving. We take pride in writing solid production code in Python, deploying and supporting model services and pipelines, and pushing the boundaries in latency, throughput, and scalability.
Trust and Safety (T\&S) is an integral part of the Catalog Data Science Org, responsible for maintaining customer trust in the Walmart marketplace. We employ state\-of\-the\-art GenAI and ML models to identify products that violate Walmart's marketplace policies. Our end\-to\-end ML pipelines are designed to scale and detect policy violations across hundreds of violation classes and billions of catalog items — ensuring a safe marketplace for our customers. Our work carries high visibility, directly impacting marketplace growth and compliance at Walmart.
As a Senior Data Scientist (Machine Learning Engineer) on the Trust and Safety team, you will collaborate with other Data Scientists and ML Engineers to develop, deploy, and scale machine learning models in production. You will play a key role in building the next generation of our compliance detection platform — driving model serving, pipeline reliability, and the adoption of GenAI\-powered solutions to more accurately detect items that violate compliance policies.
What you'll do…
- Design and deploy production\-grade ML systems for Walmart's Catalog Trust \& Safety platform — spanning classification, detection, and segmentation
- Apply GenAI, NLP, and Computer Vision techniques to build and continuously improve models for compliance detection, content moderation, and policy violation classification
- Own the full model lifecycle — from experimentation and offline evaluation through serving, monitoring, and iterative improvement in production
- Build and optimize high\-throughput batch and real\-time inference pipelines using frameworks like Ray, Triton, and vLLM, with a focus on latency, cost, and reliability
- Drive ML architecture decisions — including model selection, distillation, quantization, and serving strategies
- Partner with Compliance, Product, and Operations teams to translate business requirements into model KPIs, evaluation frameworks, and measurable impact
- Establish and enforce ML engineering best practices across the team: reproducible training, robust evaluation datasets, versioned artifacts, and production readiness standards
- Contribute to the broader ML engineering community at Walmart through technical documentation, internal talks, and cross\-team knowledge sharing
What you'll bring…
- PhD or Master's in Computer Science, or equivalent experience; 3\+ years building and deploying production ML systems at scale
- Deep expertise in model serving and inference optimization — experience with Triton Inference Server, vLLM, TorchServe, or comparable frameworks
- Hands\-on experience with Generative AI technologies: LLMs, multimodal models, RAG architectures, prompt engineering, and fine\-tuning (LoRA/QLoRA, PEFT)
- Strong foundation in classical ML, deep learning, and modern architectures — CNNs, Transformers, and domain\-specific variants
- Proven ability to build and operate large\-scale batch and real\-time inference pipelines handling high QPS with strict latency and throughput SLAs
- Proficiency in Python and ML ecosystem tooling — PyTorch, HuggingFace, scikit\-learn, NumPy; familiarity with distributed compute frameworks (Ray, Spark)
- Experience deploying and managing ML workloads on Kubernetes; solid working knowledge of Docker, Helm, and container orchestration
- Familiarity with ML observability — model monitoring, data drift detection, performance degradation alerting, and online evaluation strategies
- Practical experience with MLOps tooling: experiment tracking (MLflow, W\&B), pipeline orchestration (Airflow, Kubeflow), and CI/CD for ML
- Hands\-on with at least one major cloud platform (GCP, Azure etc.) and comfort with managed ML services and GPU infrastructure
- Working knowledge of relational and NoSQL databases
- Experience with vector databases (Pinecone, Weaviate, pgvector) and hybrid retrieval systems for GenAI applications
- Experience with Version Control Systems, especially Git
- Strong verbal and written communication skills; ability to translate complex ML systems into clear technical and business narratives
- Proactive in tracking the latest AI/ML research and translating advancements into production\-grade solutions
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Minimum Qualifications...
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*Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.*
Option 1\- Bachelor’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field and 3 years' experience in an analytics related field. Option 2\- Master’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field and 1 years' experience in an analytics related field. Option 3 \- 5 years' experience in an analytics or related field.Preferred Qualifications...
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*Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.*
Data science, machine learning, optimization models, Master’s degree in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch), We value candidates with a background in creating inclusive digital experiences, demonstrating knowledge in implementing Web Content Accessibility Guidelines (WCAG) 2\.2 AA standards, assistive technologies, and integrating digital accessibility seamlessly. The ideal candidate would have knowledge of accessibility best practices and join us as we continue to create accessible products and services following Walmart’s accessibility standards and guidelines for supporting an inclusive culture.Primary Location...
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1375 Crossman Ave, Sunnyvale, CA 94089\-1114, United States of America
Walmart and its subsidiaries are committed to maintaining a drug\-free workplace and has a no tolerance policy regarding the use of illegal drugs and alcohol on the job. This policy applies to all employees and aims to create a safe and productive work environment.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At Walmart, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $204,700 based on 441 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Walmart AI Hiring
Walmart has 36 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer. Positions span Bentonville, AR, US, Sunnyvale, CA, US, Elwood, IL, US. Compensation range: $79K - $370K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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