Engineer for production from day one on Databricks (on GCP) -PySpark+ Delta for distributed ... Bayesian methods, reconciliation across hierarchies, calibrated probabilistic projections, and ...
Engineer for production from day one on Databricks (on GCP) -PySpark+ Delta for distributed ... Bayesian methods, reconciliation across hierarchies, calibrated probabilistic projections, and ...
Senior Data Scientist
Menlo Park, CA · On-site
$156K - $224K/yr
... probabilistic models (e.g., hierarchical models, state-space models, Bayesian approaches ... Engineering, Computer Science) or equivalent practical experience. * 8+ years of experience ...
Senior Data Scientist
Menlo Park, CA · On-site
$156K - $224K/yr
... probabilistic models (e.g., hierarchical models, state-space models, Bayesian approaches ... Engineering, Computer Science) or equivalent practical experience. * 8+ years of experience ...
... Engineering, or a related quantitative discipline. Experience with simulation systems, probabilistic modeling, Bayesian methods, survival analysis, causal impact modeling, reinforcement learning ...
... Engineering, or a related quantitative discipline. Experience with simulation systems, probabilistic modeling, Bayesian methods, survival analysis, causal impact modeling, reinforcement learning ...
(USA)Staff, Data Scientist
Cupertino, CA · On-site
$143K - $286K/yr
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
(USA)Staff, Data Scientist
Cupertino, CA · On-site
$143K - $286K/yr
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
(USA)Staff, Data Scientist
Fremont, CA · On-site
$143K - $286K/yr
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
(USA)Staff, Data Scientist
Fremont, CA · On-site
$143K - $286K/yr
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
(USA)Staff, Data Scientist
Hayward, CA · On-site
$143K - $286K/yr
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
(USA)Staff, Data Scientist
Hayward, CA · On-site
$143K - $286K/yr
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
(USA)Staff, Data Scientist
Sunnyvale, CA · On-site
$143K - $286K/yr
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
(USA)Staff, Data Scientist
Sunnyvale, CA · On-site
$143K - $286K/yr
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
(USA)Staff, Data Scientist
San Mateo, CA · On-site
$143K - $286K/yr
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
(USA)Staff, Data Scientist
San Mateo, CA · On-site
$143K - $286K/yr
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
(USA)Staff, Data Scientist
Mountain View, CA · On-site
$143K - $286K/yr
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
(USA)Staff, Data Scientist
Mountain View, CA · On-site
$143K - $286K/yr
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
(USA)Staff, Data Scientist
San Jose, CA · On-site
$143K - $286K/yr
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
(USA)Staff, Data Scientist
San Jose, CA · On-site
$143K - $286K/yr
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
(USA)Staff, Data Scientist
Milpitas, CA · On-site
$143K - $286K/yr
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
(USA)Staff, Data Scientist
Milpitas, CA · On-site
$143K - $286K/yr
... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...
Principal Data Scientist
South San Francisco, CA · On-site
$196K/yr
... engineering methods for high-dimensional glycoproteomic data * Bring a diverse modeling toolkit - classical statistical methods, tree-based ensembles, deep learning, probabilistic and Bayesian ...
Principal Data Scientist
South San Francisco, CA · On-site
$196K/yr
... engineering methods for high-dimensional glycoproteomic data * Bring a diverse modeling toolkit - classical statistical methods, tree-based ensembles, deep learning, probabilistic and Bayesian ...
... engineering methods for high-dimensional glycoproteomic data * Bring a diverse modeling toolkit classical statistical methods, tree-based ensembles, deep learning, probabilistic and Bayesian ...
Quick apply
... engineering methods for high-dimensional glycoproteomic data * Bring a diverse modeling toolkit classical statistical methods, tree-based ensembles, deep learning, probabilistic and Bayesian ...
... engineering methods for high-dimensional glycoproteomic data * Bring a diverse modeling toolkit - classical statistical methods, tree-based ensembles, deep learning, probabilistic and Bayesian ...
... engineering methods for high-dimensional glycoproteomic data * Bring a diverse modeling toolkit - classical statistical methods, tree-based ensembles, deep learning, probabilistic and Bayesian ...
Autonomy Systems Software Engineer
San Francisco, CA · On-site
$200K - $245K/yr
Strong programming skills in Python, MATLAB, C++, or C * Hands-on work across multiple of the ... Bayesian inference (e.g., beta and gamma distributions) * Markov models and probabilistic system ...
Autonomy Systems Software Engineer
San Francisco, CA · On-site
$200K - $245K/yr
Strong programming skills in Python, MATLAB, C++, or C * Hands-on work across multiple of the ... Bayesian inference (e.g., beta and gamma distributions) * Markov models and probabilistic system ...
... engineering methods for high-dimensional glycoproteomic data • Bring a diverse modeling toolkit -- classical statistical methods, tree-based ensembles, deep learning, probabilistic and Bayesian ...
... engineering methods for high-dimensional glycoproteomic data • Bring a diverse modeling toolkit -- classical statistical methods, tree-based ensembles, deep learning, probabilistic and Bayesian ...
Autonomy Systems Software Engineer
$200K - $245K/yr
Strong programming skills in Python, MATLAB, C++, or C * Hands-on work across multiple of the ... Bayesian inference (e.g., beta and gamma distributions) * Markov models and probabilistic system ...
Quick apply
Autonomy Systems Software Engineer
$200K - $245K/yr
Strong programming skills in Python, MATLAB, C++, or C * Hands-on work across multiple of the ... Bayesian inference (e.g., beta and gamma distributions) * Markov models and probabilistic system ...
Software Development Engineer - Location Technologies, Sensing & Connectivity
Cupertino, CA · On-site
... programming skills in C, C++, Objective-C, or Swift, with solid foundation in algorithms, data ... Background in signal processing, Kalman filtering, particle filters, or other probabilistic state ...
Software Development Engineer - Location Technologies, Sensing & Connectivity
Cupertino, CA · On-site
... programming skills in C, C++, Objective-C, or Swift, with solid foundation in algorithms, data ... Background in signal processing, Kalman filtering, particle filters, or other probabilistic state ...
Data Scientist
San Francisco, CA · On-site
$150K - $185K/yr
... Bayesian inference, gradient boosting, regularized regression, causal ML, and probabilistic record ... Partner with Data Engineering to define data requirements, validate pipelines, and ensure model ...
Data Scientist
San Francisco, CA · On-site
$150K - $185K/yr
... Bayesian inference, gradient boosting, regularized regression, causal ML, and probabilistic record ... Partner with Data Engineering to define data requirements, validate pipelines, and ensure model ...
Probabilistic Programming Bayesian information
What are the typical challenges faced by professionals working in Probabilistic Programming with a Bayesian focus, and how can they be addressed?
Professionals working in Probabilistic Programming with a Bayesian focus often encounter challenges related to model complexity, computational efficiency, and communicating results to non-technical stakeholders. Building accurate Bayesian models requires careful selection of priors and an understanding of underlying data distributions, which can be demanding without robust domain expertise. Additionally, computational demands can be high, especially for large datasets or complex hierarchical models, making efficient sampling and approximation methods essential. Collaborating closely with domain experts and leveraging modern probabilistic programming frameworks can help address these challenges and ensure practical, interpretable results.
What is probabilistic programming in the context of Bayesian statistics?
Probabilistic programming in the context of Bayesian statistics refers to writing computer programs that use probability distributions and Bayesian inference to model uncertainty and learn from data. These programs allow users to define complex probabilistic models using code, making it easier to specify, fit, and analyze Bayesian models. Probabilistic programming languages, such as Stan, PyMC, or Edward, provide tools to automate inference, enabling practitioners to focus on modeling rather than mathematical derivations. This approach is widely used in fields like machine learning, data science, and scientific research to handle uncertainty and make predictions.
What is the difference between Probabilistic Programming Bayesian vs Data Scientist?
| Aspect | Probabilistic Programming Bayesian | Data Scientist |
|---|---|---|
| Required credentials | Background in statistics, probability, programming | Statistics, computer science, or related degree |
| Work environment | Research, modeling, algorithm development | Data analysis, visualization, business insights |
| Industry usage | AI, machine learning, research projects | Business, finance, tech, healthcare |
Probabilistic Programming Bayesian focuses on developing models using Bayesian methods and probabilistic programming languages, often in research or AI development. Data Scientists analyze data to extract insights, build predictive models, and support decision-making. While both roles require statistical knowledge, Bayesian programmers specialize in probabilistic modeling, whereas Data Scientists apply a broader set of data analysis techniques.
What are the key skills and qualifications needed to thrive as a Probabilistic Programming Bayesian specialist, and why are they important?
To thrive as a Probabilistic Programming Bayesian specialist, you need a strong background in statistics, probability theory, and Bayesian inference, often supported by a degree in mathematics, statistics, computer science, or a related field. Expertise with probabilistic programming languages (such as Stan, PyMC, or TensorFlow Probability) and familiarity with statistical modeling software are also essential. Analytical thinking, problem-solving, and effective communication skills help translate complex models into actionable insights and collaborate with interdisciplinary teams. These skills and qualities are crucial for developing robust, interpretable models that inform decision-making in research and industry applications.
What are popular job titles related to Probabilistic Programming Bayesian jobs in California? For Probabilistic Programming Bayesian jobs in California, the most frequently searched job titles are:
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What job categories do people searching Probabilistic Programming Bayesian jobs in California look for? The top searched job categories for Probabilistic Programming Bayesian jobs in California are:
What cities in California are hiring for Probabilistic Programming Bayesian jobs? Cities in California with the most Probabilistic Programming Bayesian job openings:
Full-time
Medical, Dental, Vision, Life, Retirement, PTO
Posted 12 days ago
Walmart rating
6.0
Based on 21,840 frontline employees who took The Breakroom Quiz
22nd of 39 rated national retailers
Job description
Position Summary...Role summary
Join Merchandising Decision Sciences (MDS) as the founding Staff Data Scientist for our new External Data and Analytics Products team. You will design, build, deploy, scale and monitor the ML systems that power Walmart's view of ROM - the Rest of Market - the slice of retail that doesn't ring at our own registers but shapes every category decision we make. You will own three model families end-to-end: embedding-driven hierarchy classification, GMV distribution normalization and projection, and causal impact modeling to market share. You will be the only data scientist on the program at the start, so we need someone who can architect for scale on Databricks (on GCP) from day one, ship to production, set up the MLOps foundations, and hand a healthy, well-instrumented platform to the ML engineering team that grows in behind you. This is a builder's role with a clear runway: get the first models live, prove the lift, and shape the team that scales them.
Have you ever wondered how Walmart sees the Rest of Market - the part of retail we don't ring ourselves - and decides where to grow share next? Do you get a thrill from being the first scientist on a program: the one who picks the stack, ships the first model, sets the bar, and watches the platform you built fill up with scientists behind you? We'd love to put your end-to-end ML skills to work on one of retail's hardest measurement problems.
About the team
External Data and Analytics Products is a brand-new subteam within Merchandising Decision Sciences. We acquire, model, and productize syndicated and external data - NielsenIQ, Circana, GS1, and the rest - into analytics and ML services that merchants and systems use to make sharper, faster decisions. Our charter is to turn the noisy, fragmented view of the outside world into a calibrated signal Walmart can plan against. We work as a full-stack team and we hold ourselves to engineering-level rigor: every model we ship has an owner, a monitor, and a runbook.What you'll do...
At Walmart, we offer competitive pay as well as performance-based bonus awards and other great benefits for a happier mind, body, and wallet. Health benefits include medical, vision and dental coverage. Financial benefits include 401(k), stock purchase and company-paid life insurance. Paid time off benefits include PTO (including sick leave), parental leave, family care leave, bereavement, jury duty, and voting. Other benefits include short-term and long-term disability, company discounts, Military Leave Pay, adoption and surrogacy expense reimbursement, and more. You will also receive PTO and/or PPTO that can be used for vacation, sick leave, holidays, or other purposes. The amount you receive depends on your job classification and length of employment. It will meet or exceed the requirements of paid sick leave laws, where applicable. For information about PTO, see https://one.walmart.com/notices. Live Better U is a Walmart-paid education benefit program for full-time and part-time associates in Walmart and Sam's Club facilities. Programs range from high school completion to bachelor's degrees, including English Language Learning and short-form certificates. Tuition, books, and fees are completely paid for by Walmart.
Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to a specific plan or program terms.
For information about benefits and eligibility, see One.Walmart.
Bentonville, Arkansas US-30001: The annual salary range for this position is $110,000.00 - $220,000.00
Sunnyvale, California US-11789: The annual salary range for this position is $143,000.00 - $286,000.00 Additional compensation includes annual or quarterly performance bonuses. Additional compensation for certain positions may also include :
- StockMinimum Qualifications...
4 years' experience in data engineering, database engineering, business intelligence, or business analytics.
1 year's supervisory experience.Preferred Qualifications...
Join Merchandising Decision Sciences (MDS) as the founding Staff Data Scientist for our new External Data and Analytics Products team. You will design, build, deploy, scale and monitor the ML systems that power Walmart's view of ROM - the Rest of Market - the slice of retail that doesn't ring at our own registers but shapes every category decision we make. You will own three model families end-to-end: embedding-driven hierarchy classification, GMV distribution normalization and projection, and causal impact modeling to market share. You will be the only data scientist on the program at the start, so we need someone who can architect for scale on Databricks (on GCP) from day one, ship to production, set up the MLOps foundations, and hand a healthy, well-instrumented platform to the ML engineering team that grows in behind you. This is a builder's role with a clear runway: get the first models live, prove the lift, and shape the team that scales them.
Have you ever wondered how Walmart sees the Rest of Market - the part of retail we don't ring ourselves - and decides where to grow share next? Do you get a thrill from being the first scientist on a program: the one who picks the stack, ships the first model, sets the bar, and watches the platform you built fill up with scientists behind you? We'd love to put your end-to-end ML skills to work on one of retail's hardest measurement problems.
About the team
External Data and Analytics Products is a brand-new subteam within Merchandising Decision Sciences. We acquire, model, and productize syndicated and external data - NielsenIQ, Circana, GS1, and the rest - into analytics and ML services that merchants and systems use to make sharper, faster decisions. Our charter is to turn the noisy, fragmented view of the outside world into a calibrated signal Walmart can plan against. We work as a full-stack team and we hold ourselves to engineering-level rigor: every model we ship has an owner, a monitor, and a runbook.What you'll do...
What you'll do
- Design, build, deploy, and monitorembedding-based classification models that align external product signals to the Walmart merchandising hierarchy- from candidate generation and ANN retrieval through fine-tuned classifiers and human-in-the-loop feedback for long-tail nodes.
- DevelopGMV distribution normalization and projection modelsthat reconcile heterogeneous internal and external GMV signals across categories, time, and geography - and produce projections business partners can plan against.
- Buildcausal impact models that quantify market-share movementfrom merchandising actions (assortment, pricing, promo, distribution) using methods such as difference-in-differences, synthetic control, Bayesian structural time series, and uplift modeling - and clearly communicate assumptions, sensitivity, and confidence to non-technical leaders.
- Engineer for production from day one onDatabricks (on GCP)-PySpark+ Delta for distributed training and inference,MLflowfor tracking and registry, Unity Catalog for governance, Databricks Model Serving and Jobs for deployment,BigQuery,Dataprocand Vertex AI where they fit best.
- Establish theMLOps foundationsthe ROM platform will live on: CI/CD for models, feature management, drift and quality monitoring, retraining triggers, shadow deployments, model cards, and on-call runbooks - so the ML engineers who join behind you can scale the platform without re-platforming it.
- Own theend-to-end ML lifecyclefor every model you put in production - problem framing, data contracts, training, evaluation, deployment, monitoring, retraining, and incident response.
What you'll bring
- Extensive industry experience as a hands-on data scientist who has personally taken ML systems from notebook to production at scale and stayed on them through monitoring, drift, and retraining.
- Deep, hands-on experienceshipping and scaling ML on Databricks- PySpark, Delta, MLflow (tracking and registry), Unity Catalog, Databricks Jobs and Workflows, and Databricks Model Serving. You know where Databricks shines and where to reach for something else.
- Strong production fluency withGCP- BigQuery, GCS, Vertex AI, Cloud Run, Composer/Airflow - and the ability to wire Databricks and GCP services together cleanly.
- Proven expertise withvector embeddings: training, fine-tuning, and evaluating embedding models for retail/product data; pairing embeddings with classifiers; ANN retrieval and vector indexing at catalog scale; choosing the right embedding model for the right job.
- Deep expertise insupervised classification at scale, including tree ensembles (XGBoost / LightGBM), embedding-based classifiers, and transformer fine-tuning; comfort with severe class imbalance, noisy labels, hierarchy-aware loss design, and long-tail evaluation.
- Strong command offorecasting and distribution modeling- hierarchical and Bayesian methods, reconciliation across hierarchies, calibrated probabilistic projections, and normalization across heterogeneous data sources.
- Solidcausal inferencechops for observational retail/commercial data - difference-in-differences, synthetic control, propensity methods, Bayesian structural time series (e.g., CausalImpact), and uplift / heterogeneous treatment effects.
- StrongMLE instincts: containerization, CI/CD for models, infrastructure-as-code where it matters, observability for ML systems, and a healthy respect for production discipline. You write code that another engineer can read, test, and extend.
- Expert-levelPython and SQL; comfortable in distributed compute (PySpark) and able to optimize a stubborn job.
- Excellent written and verbal communication - you can explain an embedding loss to an MLE andcausalestimatesto a merchant in the same afternoon.
- Familiarity withsyndicated external datasets(e.g., NielsenIQ, Circana) and publicly available data and frameworks (e.g., GS1) is a strong plus.
You'll sweep us off our feet if...
- You've been thefounding or solo data scientiston a program before - you've picked the first tools, written the first design docs, shipped the first model, and handed a healthy codebase to the team that came after you.
- You'vestood up the MLOps house on Databricks from scratch- MLflow registry, Unity Catalog, model serving, monitoring - and can show the pull requests to prove it.
- You'vescaled an embedding-driven classifier on a real retail or e-commerce catalog(millions of SKUs, long-tail nodes, drift over time) and you know exactly where it tends to break.
- You operate withengineering-level rigor- your notebooks turn into modules, your modules turn into services, and your services have tests, alerts, and runbooks.
- You're astrong storytellerwho can move a room of merchants and execs by connecting a model output to a market-share point - and equally comfortable in a code review defending an evaluation choice.
- You think like aproduct owner: you know which model is worth shipping, which one is worth killing, and which problem isn't a modeling problem at all.
- You'vefelt the pain of a model in production at 2 a.m. andbuilt the kind of monitoring and guardrails that mean youdon'tfeel it twice.
At Walmart, we offer competitive pay as well as performance-based bonus awards and other great benefits for a happier mind, body, and wallet. Health benefits include medical, vision and dental coverage. Financial benefits include 401(k), stock purchase and company-paid life insurance. Paid time off benefits include PTO (including sick leave), parental leave, family care leave, bereavement, jury duty, and voting. Other benefits include short-term and long-term disability, company discounts, Military Leave Pay, adoption and surrogacy expense reimbursement, and more. You will also receive PTO and/or PPTO that can be used for vacation, sick leave, holidays, or other purposes. The amount you receive depends on your job classification and length of employment. It will meet or exceed the requirements of paid sick leave laws, where applicable. For information about PTO, see https://one.walmart.com/notices. Live Better U is a Walmart-paid education benefit program for full-time and part-time associates in Walmart and Sam's Club facilities. Programs range from high school completion to bachelor's degrees, including English Language Learning and short-form certificates. Tuition, books, and fees are completely paid for by Walmart.
Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to a specific plan or program terms.
For information about benefits and eligibility, see One.Walmart.
Bentonville, Arkansas US-30001: The annual salary range for this position is $110,000.00 - $220,000.00
Sunnyvale, California US-11789: The annual salary range for this position is $143,000.00 - $286,000.00 Additional compensation includes annual or quarterly performance bonuses. Additional compensation for certain positions may also include :
- StockMinimum Qualifications...
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 Computer Science and 5 years' experience in software engineering or related field. Option 2: 7 years' experience in software engineering or related field. Option 3: Master's degree in Computer Science and 3 years' experience in software engineering or related field.4 years' experience in data engineering, database engineering, business intelligence, or business analytics.
1 year's supervisory experience.Preferred Qualifications...
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, PhD 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...601 Respect Dr, Bentonville, AR 72716, United States of AmericaWalmart 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.
About Walmart
Sourced by ZipRecruiter
From our humble beginnings as a small discount retailer in Rogers, Ark., Walmart has opened thousands of stores in the U.S. and expanded internationally. Through innovation, we're creating a seamless experience to let customers shop anytime and anywhere online and in stores. We are creating opportunities and bringing value to customers and communities around the globe. Walmart operates approximately 10,500 stores and clubs in 19 countries and eCommerce websites. We employ 2.1 million associates around the world — nearly 1.6 million in the U.S. alone.
Industry
Retail and transportation and warehousing
Company size
10,000+ Employees
Headquarters location
Bentonville, AR, US