(usa) Staff, Data Scientist

Walmart Walmart · Retail · Bentonville, AR

Staff Data Scientist role at Walmart focused on building scalable, end-to-end data science solutions for Walmart Marketplace. Responsibilities include owning the MLOps lifecycle, developing MLOps infrastructure, applying SRE principles to ML workloads, leading code quality, providing architectural guidance, building scalable training and inference pipelines, enhancing data feedback workflows, and deploying end-to-end models. Requires strong knowledge of ML, statistics, Python, big data platforms, Git, and SQL.

What you'd actually do

  1. Own the MLOps lifecycle, from data monitoring to refactoring data science code to building robust model monitoring workflows for model lifecycle management
  2. Can develop and maintain scalable MLOps infrastructure, such as building Kafka pipeline for unified logging and monitoring across multiple projects.
  3. Apply SRE (Site Reliability Engineering) principles for ML workloads, optimizing cloud deployments, monitoring performance and troubleshooting application latency or operational issues.
  4. Lead code quality and engineering best practices by conducing code reviews and mentoring junior MLEs.
  5. Build continuous, distributed and scalable training pipeline, inference pipeline and performance monitoring pipeline to be used across several Marketplace initiatives.

Skills

Required

  • Machine learning
  • statistics
  • supervised learning
  • unsupervised learning
  • deep learning
  • Python
  • Data Structures
  • big data platforms
  • Hadoop
  • Hive
  • Pig
  • Map Reduce
  • Scala
  • Spark
  • Git
  • SQL
  • relational databases
  • data warehouse

Nice to have

  • Kafka pipeline
  • SRE principles for ML workloads
  • cloud deployments
  • model monitoring workflows
  • code reviews
  • mentoring junior MLEs
  • architectural project guidance

What the JD emphasized

  • scalable end-to-end data science solutions
  • MLOps lifecycle
  • MLOps infrastructure
  • scalable training pipeline
  • inference pipeline
  • performance monitoring pipeline
  • end-to-end models

Other signals

  • MLOps lifecycle
  • MLOps infrastructure
  • SRE principles for ML workloads
  • scalable training pipeline
  • inference pipeline
  • performance monitoring pipeline
  • end-to-end models