Senior Data Scientist – AI & ML | Mlops Enablement (hybrid – Seattle, Wa)

Nordstrom Nordstrom · Retail · Seattle, WA

Senior Data Scientist role focused on MLOps enablement and platform engineering within Nordstrom's Developer Platform organization. The role acts as a bridge between Data Science practice and platform engineering, shaping the ML platform infrastructure and tooling that Data Scientists use. Key responsibilities include validating new platform capabilities, designing the Model Evaluation Framework, building model-type-aware configurations, benchmarking platforms, and contributing to the Vertex AI Agentic Platform. Requires deep expertise in model evaluation, feature stores, ML monitoring, and production model delivery, with a strong preference for GCP/Vertex AI experience.

What you'd actually do

  1. Run end-to-end POC validation for new platform capabilities — Feature Store, Endpoints, Model Evaluation, AutoML, BigQuery ML etc. — independently, before they reach DS teams at scale
  2. Attend DS team planning and design sessions as an embedded practitioner; surface real workflow pain points and translate them into reusable MLOps platform requirements
  3. Design and own the Model Evaluation Framework — defining metrics, thresholds, and evaluation pipelines for batch, online, and streaming use cases on Vertex AI
  4. Build model-type-aware Feature Store schemas, endpoint configurations, and evaluation pipelines that accommodate the fundamentally different needs of different ML models
  5. Lead benchmarking of Nordstrom’s platform against industry standards — SageMaker vs. Vertex AI — across feature parity, cost, and DS practitioner ergonomics

Skills

Required

  • Bachelor’s, Master’s, or PhD in Statistics, Data Science, Computer Science, Engineering, or a related technical field
  • 10+ years of hands-on Data Science experience with production model delivery across multiple ML (classification, ranking, NLP, time-series, recommendation) and GenAI models
  • Deep expertise in model evaluation — defining metrics, thresholds, and evaluation pipelines for real-world production models
  • Experience with Feature Store design, feature engineering, and understanding of feature freshness, reuse, and drift across different model families
  • Proficiency in Python with experience writing clean, maintainable, production-quality ML code
  • Strong understanding of ML monitoring — data drift, prediction drift, and concept drift detection
  • Experience with experiment tracking and model lifecycle management
  • Ability to translate between DS practice and platform engineering — comfortable driving design decisions, authoring DS-native documentation, and engaging in technical design reviews
  • Self-directed; comfortable owning POC work end-to-end without a dedicated DS team structure

Nice to have

  • Hands-on experience with GCP and Vertex AI — Workbench, Pipelines, Feature Store, Model Endpoints, Model Registry, Model Evaluation
  • Familiarity with AWS SageMaker for cross-cloud benchmarking and comparison context
  • Understanding of CI/CD for ML, containerization, and pipeline orchestration — able to engage at platform depth alongside MLOps engineers
  • Prior experience in ML platform adoption, enablement, or developer experience work
  • Experience operating within a mature ML lifecycle — versioning, lineage tracking, model governance, staged rollouts, and model deprecation practices at enterprise scale
  • Exposure to agentic AI patterns, LLM evaluation frameworks, or Vertex AI Agent Builder

What the JD emphasized

  • production model delivery
  • model evaluation
  • Feature Store design
  • ML monitoring
  • experiment tracking and model lifecycle management
  • platform engineering
  • GCP and Vertex AI
  • SageMaker
  • CI/CD for ML, containerization, and pipeline orchestration
  • ML platform adoption, enablement, or developer experience work
  • mature ML lifecycle
  • agentic AI patterns, LLM evaluation frameworks, or Vertex AI Agent Builder

Other signals

  • MLOps Enablement team owns the ML Platform capability
  • Data Scientists and engineers can build, deploy, and operate machine learning models on managed, standards-compliant infrastructure
  • embedded DS practitioner
  • platform-facing role for a DS practitioner who wants to shape the infrastructure and tooling
  • Design and own the Model Evaluation Framework
  • Build model-type-aware Feature Store schemas, endpoint configurations, and evaluation pipelines
  • Lead benchmarking of Nordstrom’s platform against industry standards
  • Contribute DS domain expertise to the emerging Vertex AI Agentic Platform
  • Own model card standards
  • 10+ years of hands-on Data Science experience with production model delivery across multiple ML (classification, ranking, NLP, time-series, recommendation) and GenAI models
  • Deep expertise in model evaluation
  • Experience with Feature Store design
  • Strong understanding of ML monitoring
  • Experience with experiment tracking and model lifecycle management
  • Ability to translate between DS practice and platform engineering
  • Hands-on experience with GCP and Vertex AI
  • Familiarity with AWS SageMaker
  • Understanding of CI/CD for ML, containerization, and pipeline orchestration
  • Prior experience in ML platform adoption, enablement, or developer experience work
  • Experience operating within a mature ML lifecycle
  • Exposure to agentic AI patterns, LLM evaluation frameworks, or Vertex AI Agent Builder