Senior, Data Scientist

Retail AI Foundations is part of the Retail Intelligence group within Walmart’s AI & Data organization. The team develops and deploys Generative AI and machine learning platforms, models, and services across Walmart’s highest-impact growth pillars—including AI Shopping, Social Commerce, Walmart Connect advertising, creator experiences, marketing, and rich-media content.

Our Applied Science and ML Engineering teams build AI capabilities across Large Language Models (LLMs), image and video generation, and multimodal understanding. We partner with scientists, engineers, product managers, and business leaders to improve experiences for customers, associates, advertisers, and creators, while delivering measurable business outcomes. We are committed to exceeding industry standards through cutting-edge research, strong engineering execution, and responsible AI practices.

As a Senior Data Scientist within Retail AI Foundations, you will develop, evaluate, and deploy advanced AI models and algorithms that power Walmart’s next-generation customer and business experiences. You will apply applied-science rigor to turn complex customer and business problems into scalable, measurable AI solutions.

This role combines hands-on model development with cross-functional influence. You will work directly with ML Engineers, product managers, data teams, and business partners to define success measures, build and validate models, and improve their performance in production.

• Build, evaluate, and improve ML and Generative AI models across LLMs, image and video generation, multimodal understanding, and agentic systems.
• Develop data and evaluation strategies, including dataset curation, exploratory analysis, feature engineering, error analysis, ground truth, and offline evaluation.
• Design and execute model experiments, benchmarking, and A/B tests to measure quality, safety, reliability, latency, scalability, and business impact.
• Apply post-training, fine-tuning, retrieval, prompting, and other optimization techniques to improve model performance for production use cases.
• Partner with product, engineering, data, and business teams to translate customer and business opportunities into technical requirements, success metrics, and deployable solutions.
• Work with ML Engineers to deploy, monitor, and iterate on models in large-scale production environments.
• Communicate technical recommendations and model results to stakeholders, while mentoring junior scientists and engineers on rigorous experimentation and responsible AI practices.

• Strong hands-on expertise in Python, machine learning, deep learning, NLP, information retrieval, computer vision, multimodal AI, and model evaluation.
• Ability to translate customer and business needs into model designs, experiments, technical requirements, and scalable production solutions; communicate findings and tradeoffs clearly across product, engineering, data, business, and leadership teams.
• Experience adapting open-weight models for specific business use cases through model selection, post-training, fine-tuning, and evaluation, including LLM-, vision-, multimodal-, and agent-based systems.
• Experience benchmarking models and participating in A/B tests; optimizing training and inference architectures across quality, cost, latency, throughput, scalability, and reliability through techniques such as prompt optimization, custom loss functions, reasoning models, and test-time scaling.
• Experience creating high-quality retail training and evaluation data, including dataset sourcing and curation, unsupervised or weakly supervised learning, synthetic-data approaches, and ground-truth development with crowd sourcing and annotation partners.
• Experience with…