(usa) Principal, Data Scientist - Generalist

Walmart Walmart · Retail · Sunnyvale, CA +1

Principal Data Scientist to lead AI explorations, research, and algorithmic solution creation for Sam's Club division. Focus on developing foundational ML/AI models for member experience across eCommerce, Scan & Go, and in-club shopping. Oversee end-to-end implementation of ML products in Search and Ads, guide a scrum team, and collaborate with Product Managers and Technologists to implement ML/AI solutions at scale. Integrate retail knowledge with ML algorithms to drive growth. Role involves leading a team focused on backend software infrastructure for AI/ML solutions, partnering with leaders on innovations in discovery experiences, and executing full project lifecycles including MLOps framework development for continuous deployment and personalization.

What you'd actually do

  1. Leading a team of Data Scientists and Engineers focused on backend software infrastructure for powering AI/ML solutions
  2. Partner closely with Product and engineering leaders to inform, drive and accelerate innovations in discovery experiences via Insights, frameworks, causal inference solutions and machine learning prototypes.
  3. Executing full life cycle of projects through project planning, data collection, model prototyping and deployment, with responsibilities encompassing stakeholder management and communication to cross-functional partners.
  4. Building MLOps framework to create, build and deploy data science algorithms continuously test and personalize user experience

Skills

Required

  • Python, Java, R or Scala
  • Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib
  • Azure, Google Cloud, etc.
  • git
  • JIRA
  • Collaborative Filtering, Matrix Factorization, Factorization Machines, Word2vec, Deep Neural Networks, Wide and Deep etc
  • Search, information retrieval and ranking systems
  • MLOps practices
  • agile methodologies

Nice to have

  • Knowledge of Search, information retrieval and ranking systems
  • experience building large scale ML systems in public cloud like Azure, Google etc
  • 3 years+ of experience in applied machine learning
  • Large-scale training using data structures and algorithms
  • experience with cloud environments such as Azure, Google Cloud, etc.
  • Solid MLOps practices including good design documentation, unit testing, integration testing and source code control (git).
  • experienced with agile methodologies using project planning and tracking management tools e.g., JIRA

What the JD emphasized

  • architect
  • leading AI explorations
  • algorithmic solution creation
  • fast prototyping
  • foundational ML/AI models
  • end-to-end implementation
  • Machine Learning products
  • Search and Ads
  • implement ML/AI solutions at scale
  • retail industry knowledge
  • machine learning algorithms
  • ambitious growth objectives
  • discovery experiences
  • Insights, frameworks, causal inference solutions
  • machine learning prototypes
  • stakeholder management
  • communication to cross-functional partners
  • MLOps framework
  • continuously test and personalize user experience
  • backend software infrastructure
  • powering AI/ML solutions
  • large-scale production systems
  • Knowledge of Search, information retrieval and ranking systems
  • building large scale ML systems
  • public cloud like Azure, Google etc
  • applied machine learning
  • Collaborative Filtering, Matrix Factorization, Factorization Machines, Word2vec, Deep Neural Networks, Wide and Deep etc
  • Programming language (Python, Java, R or Scala)
  • Large-scale training
  • data structures and algorithms
  • Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib
  • cloud environments such as Azure, Google Cloud, etc.
  • Solid MLOps practices
  • good design documentation
  • unit testing
  • integration testing
  • source code control (git)
  • agile methodologies
  • project planning
  • tracking management tools e.g., JIRA
  • solving challenges at the scale of a Fortune 1 Retailer

Other signals

  • leading AI explorations
  • algorithmic solution creation
  • ML/AI models that will drive a tailored and enjoyable member experience
  • end-to-end implementation of Machine Learning products
  • implement ML/AI solutions at scale
  • integrate retail industry knowledge with machine learning algorithms
  • leading impactful and transformative shifts in our business
  • backend software infrastructure for powering AI/ML solutions
  • innovations in discovery experiences
  • MLOps framework to create, build and deploy data science algorithms continuously test and personalize user experience