Senior Director, Data Science

Walmart Walmart · Retail · Crossman Respect Building CA SUNNYVALE, Bentonville, AR

Senior Director of Data Science for Commerce Foundational Models at Walmart. Responsible for the vision, strategy, and execution of large-scale foundational models trained on massive, multi-modal datasets. These models will power critical consumer-facing and enterprise products across Walmart's ecosystem, including Search, Personalization, Sponsored Ads, Conversational Commerce, and Supply Chain Forecasting. The role involves leading a team of Applied AI Researchers, ML Engineers, and Data Scientists, defining the multi-year roadmap, and collaborating with product and engineering leaders.

What you'd actually do

  1. Define the multi-year roadmap for Walmart’s Commerce Foundational Models. Drive the paradigm shift from siloed, domain-specific models to unified, multi-task generative architectures
  2. Lead a world-class team of Applied AI Researchers, Machine Learning Engineers, and Data Scientists.
  3. Partner closely with product, engineering, and business leaders across Search, Ads, Personalization, and Customer Experience to ensure foundational models are seamlessly integrated, delivering massive business value.
  4. Serve as the primary champion for foundational models within Walmart Global Tech.

Skills

Required

  • Master’s degree in Computer Science, Machine Learning, Statistics, Mathematics, or a highly quantitative field
  • 8+ years of experience directly managing and scaling high-performing data science and ML engineering teams
  • 3+ years managing managers
  • Proven track record of designing, training, and deploying deep learning models in production at massive scale
  • Deep understanding of distributed training frameworks

Nice to have

  • Ph.D. in CS, ML, or a related field with a strong publication record in top-tier AI/ML conferences
  • Domain Expertise in Commerce/RecSys
  • experience building Generative Recommenders, Hierarchical Sequential Transducers(HSTU) or sequence-based models for user behavior representation
  • Deep knowledge of low-latency inference optimization techniques
  • real-time retrieval

What the JD emphasized

  • managing and scaling high-performing data science and ML engineering teams
  • managing managers
  • designing, training, and deploying deep learning models in production at massive scale
  • Deep understanding of distributed training frameworks
  • low-latency inference optimization techniques
  • real-time retrieval

Other signals

  • building foundational models
  • large-scale training
  • production deployment
  • multi-modal dataset
  • consumer facing products