Senior Data Scientist – Supply Chain Optimization

Walmart • Bentonville, AR

1d • $110,302 - $180,000 • Onsite

About The Position

The Senior Data Scientist – Supply Chain Optimization designs and deploys intelligent, goal-driven agents to optimize customer delivery promises, fulfillment routing, and root-cause analysis across end-to-end supply-chain workflows. The role builds agents with reasoning, planning, tool usage, and short- and long-term memory using frameworks such as LangChain, LangGraph, Google ADK, and Pydantic. It leverages state-of-the-art LLMs and trainable model architectures to create NLP solutions, including context engineering, chain-of-thought reasoning, and tool calling, and deploys AI/ML and agentic systems into production.

The position develops scalable RAG systems for real-time access to logistics policies, SOPs, and network constraints; manages vector databases and knowledge graphs; and applies machine-learning methods to structured and unstructured data to improve delivery ETA prediction and reliability. It builds scalable data-processing and modeling solutions, optimizes workflows using PostgreSQL and Google BigQuery, and develops production evaluation and observability frameworks to assess model and agent performance, decision accuracy, hallucination rates, latency, and cost. The role also translates advances in AI research into scalable enterprise solutions for the end-to-end supply chain.

The position is located at 811 Excellence Dr, Bentonville, AR 72716. Minimum qualifications are a bachelor’s degree or equivalent in Computer Science, Analytics, Engineering, or a related field plus one year of experience in analytics or a related field; alternatively, a master’s degree or equivalent in one of those fields. The employer will accept any amount of graduate coursework, graduate research experience, or professional experience with the required skills.

Requirements

• Bachelor’s degree or equivalent in Computer Science, Analytics, Engineering (any), or a related field plus one year of experience in analytics or a related field; alternatively, a master’s degree or equivalent in one of those fields.
• Experience building deep-learning model architectures in Python using PyTorch, TensorFlow, and LangChain.
• Experience pre-training or fine-tuning language models from HuggingFace or TFHub for NLP tasks such as natural-language inference and question answering using open-source corpora such as SQuAD and MNLI.
• Experience with generative models such as OpenAI GPT and encoder-based models such as BERT, RoBERTa, and DistilBERT for language-based reasoning problems.
• Experience with computational semantics tools such as WordNet, SpaCy, and NLTK.
• Experience applying advanced statistical and modeling methods, including graphical models, Bayesian inference, neural networks, SVM, and Random Forest, using scikit-learn and scipy.
• Experience with model assessment and validation techniques, including Chi square, ROC curve, and root mean square error.
• Experience performing trend, sentiment, and cluster analysis, using methods and tools including Word2Vec, TFIDF, KMeans, hierarchical clustering, PCA, and FAMD.
• Experience conducting exploratory data analysis using Pandas, NumPy, Matplotlib, and Plotly.
• Experience with CI/CD, testing, and software-development documentation, including UML, state and activity workflows, Astah, and Java.
• Employer will accept any amount of graduate coursework, graduate research experience, or professional experience with the required skills.

Responsibilities

• Design and deploy intelligent, goal-driven agents to optimize customer delivery promises, fulfillment routing, and root-cause analysis across supply-chain workflows.
• Build agents that support reasoning, planning, tool use, and short- and long-term memory using frameworks such as LangChain, LangGraph, Google ADK, and Pydantic.
• Use LLMs and trainable model architectures to build NLP solutions, including context engineering, chain-of-thought reasoning, and tool calling.
• Deploy AI/ML and agentic systems into production environments.
• Use Docker, Kubernetes, GCP or Azure, and Airflow to support scalability, reliability, and low latency, with CI/CD pipelines for continuous improvement.
• Build scalable RAG systems for real-time access to logistics policies, SOPs, and network constraints; manage vector databases and knowledge graphs to improve decision accuracy.
• Apply classical and advanced machine-learning techniques to large-scale structured and unstructured datasets to improve delivery ETA prediction and reliability.
• Use Python data-science ecosystems for scalable data processing and modeling.
• Build and optimize workflows using PostgreSQL and Google BigQuery, ensuring data quality, consistency, and performance.
• Develop evaluation and observability frameworks to measure model and agent performance in production, including decision accuracy, hallucination rates, latency, and cost.
• Translate advances from leading AI research into scalable, production-ready enterprise solutions for the end-to-end supply chain.
• Perform trend, sentiment, and cluster analyses to answer business problems and provide recommendations and insights.
• Conduct exploratory data analysis and perform model testing, tuning, and validation.

Benefits

• Competitive pay and performance-based incentive awards, including annual or quarterly performance incentives.
• Medical, vision, and dental coverage.
• 401(k), stock purchase plan, and company-paid life insurance.
• Paid time off, including sick leave, parental leave, family care leave, bereavement leave, jury duty leave, and voting leave.
• Short-term and long-term disability coverage.
• Education assistance, including 100% company-paid college degrees.
• Company discounts.
• Military service pay.
• Adoption expense reimbursement.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior