Senior Data Scientist – Supply Chain Optimization

Walmart•Bentonville, AR

14h•Onsite

About The Position

The Senior Data Scientist – Supply Chain Optimization designs and deploys intelligent, goal-driven agents for customer delivery promises, fulfillment routing, and root-cause analysis across end-to-end supply chain workflows. The role builds agents that can reason, plan, use tools, and manage short- and long-term memory, using frameworks such as LangChain, LangGraph, Google ADK, and Pydantic. It leverages state-of-the-art large language models and trainable model architectures to develop NLP solutions, including context engineering, chain-of-thought reasoning, and tool calling. The position deploys AI/ML and agentic systems in production using Docker, Kubernetes, cloud platforms such as GCP and Azure, Airflow, and CI/CD pipelines. It builds scalable retrieval-augmented generation systems and manages vector databases and knowledge graphs to provide access to logistics policies, standard operating procedures, and network constraints. The role applies classical and advanced machine learning to large structured and unstructured datasets, develops data processing and modeling solutions in Python, and optimizes PostgreSQL and Google BigQuery workflows. The Senior Data Scientist also develops production evaluation and observability frameworks to measure model and agent performance, decision accuracy, hallucination rates, latency, and cost. The role translates advances in AI research into scalable enterprise solutions that improve the end-to-end supply chain.

Requirements

Bachelor’s degree or equivalent in Computer Science, Analytics, Engineering, or a related field plus at least 1 year of experience in analytics or a related field; alternatively, a master’s degree or equivalent in one of these fields.
Experience building deep learning model architectures in Python using PyTorch, TensorFlow, and LangChain.
Experience pre-training or fine-tuning language models from Hugging Face or TFHub for NLP tasks such as natural language inference and question answering, using open-source corpora such as SQuAD and MNLI.
Experience using 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, SVMs, and Random Forest, using scikit-learn and SciPy.
Experience with model assessment and validation, including testing, tuning, and validation techniques such as chi-square, ROC curves, and root mean square error.
Experience performing trend, sentiment, and cluster analysis to address business problems and provide recommendations and insights; includes embeddings such as Word2Vec and TF-IDF, clustering such as K-Means and hierarchical clustering, and dimensionality reduction such as 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.

Responsibilities

Design and deploy intelligent agents to optimize customer delivery promises, fulfillment routing, and root-cause analysis across supply chain workflows.
Build agents with reasoning, planning, tool-use, and short- and long-term memory capabilities.
Leverage large language models 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, cloud platforms, Airflow, and CI/CD pipelines to support scalable, reliable, low-latency systems and continuous improvement.
Build scalable retrieval-augmented generation systems for real-time access to logistics policies, SOPs, and network constraints.
Manage vector databases and knowledge graphs to improve decision accuracy in fulfillment, routing, and issue resolution.
Apply machine learning techniques to large-scale structured and unstructured datasets to improve delivery ETA prediction and reliability.
Use Python tools for scalable data processing and modeling.
Build and optimize PostgreSQL and Google BigQuery workflows while ensuring data quality, consistency, and performance.
Develop evaluation and observability frameworks to measure model and agent performance, decision accuracy, hallucination rates, latency, and cost.
Translate developments in AI research into scalable, production-ready enterprise solutions that improve the end-to-end supply chain.