(USA) Senior, Data Scientist

Walmart•Sunnyvale, CA

About The Position

Retail AI Foundations is part of Walmart’s Retail Intelligence group within its AI & Data organization. The team develops and deploys generative AI and machine learning platforms, models, and services for growth areas including AI Shopping, Social Commerce, Walmart Connect advertising, creator experiences, marketing, and rich-media content. Its Applied Science and ML Engineering teams work on large language models, image and video generation, and multimodal understanding, collaborating across disciplines to improve experiences and deliver measurable business outcomes while following responsible AI practices. As a Senior Data Scientist, you will develop, evaluate, and deploy advanced AI models and algorithms for customer and business experiences. You will apply scientific rigor to complex problems and build scalable, measurable AI solutions. The role combines hands-on model development with cross-functional collaboration and influence, working with ML engineers, product managers, data teams, and business partners to define success measures, validate models, and improve production performance.

Requirements

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, and to communicate findings and tradeoffs across product, engineering, data, business, and leadership teams.
Experience adapting open-weight models to 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, and optimizing training and inference architectures for quality, cost, latency, throughput, scalability, and reliability.
Experience creating retail training and evaluation data, including dataset sourcing and curation, unsupervised or weakly supervised learning, synthetic-data approaches, and ground-truth development with crowdsourcing and annotation partners.
Experience with distributed training and inference, large-scale model serving, and massively parallel GPU systems.
Ability to use AI coding assistants effectively and build production agentic AI applications that orchestrate models, tools, and workflows.
One of the following: bachelor’s degree in a listed or related field and 3 years of analytics-related experience; master’s degree in a listed or related field and 1 year of analytics-related experience; or 5 years of experience in an analytics or related field.

Nice To Haves

4 or more years of experience building machine learning models or developing algorithms for business applications.
Master’s or PhD in Computer Science, Electrical Engineering, Machine Learning, Engineering, Robotics, Operations Research, Statistics, Mathematics, or a related quantitative field, or equivalent practical experience.
Experience with LLM post-training and alignment, including supervised fine-tuning, reinforcement learning, RLHF, DPO, or related techniques.
Experience with image or video generation models, multimodal understanding models, or production computer-vision systems.
Experience building, evaluating, and operating LLM- or agent-based systems at production scale.
Experience in data science, machine learning, or optimization models.
Master’s degree in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, or Econometrics.
Successful completion of one or more assessments in Python, Spark, Scala, or R.
Experience using open-source frameworks such as scikit-learn, TensorFlow, or Torch.
Background in creating inclusive digital experiences, with knowledge of WCAG 2.2 AA standards, assistive technologies, and digital accessibility best practices.

Responsibilities

Build, evaluate, and improve machine learning and generative AI models involving 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 conduct 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 production model performance.
Collaborate with product, engineering, data, and business teams to translate 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.
Mentor junior scientists and engineers in rigorous experimentation and responsible AI practices.

Benefits

Medical, vision, and dental coverage.
401(k), stock purchase, and company-paid life insurance.
Paid time off, including sick leave, parental leave, family care leave, bereavement, jury duty, and voting leave.
Short- and long-term disability.
Company discounts.
Military Leave Pay.
Adoption and surrogacy expense reimbursement.
Walmart-paid Live Better U education benefit, with tuition, books, and fees covered for eligible associates.
Performance-based bonus awards, including annual or quarterly bonuses.
Stock may be included as additional compensation for certain positions.