Data Scientist – Merchandising & Inventory Machine Learning - Hybrid – Seattle, Wa

Nordstrom Nordstrom · Retail · Seattle, WA

Data Scientist role focused on merchandising and inventory optimization using machine learning and predictive modeling. Responsibilities include data analysis, building and deploying ML models for demand forecasting, assortment, and pricing, and collaborating with cross-functional teams. Requires Python, SQL, and experience with ML algorithms and evaluation techniques.

What you'd actually do

  1. Perform exploratory data analysis and statistical modelling to extract actionable insights from large and complex datasets
  2. Design, develop and implement scalable and production-ready machine learning models / optimization algorithms for areas such as demand forecasting, assortment, and pricing optimization.
  3. Implement robust evaluation and monitoring to validate the performance and reliability of machine learning models
  4. Communicate complex findings, insights and trade-offs to technical and non-technical stakeholders

Skills

Required

  • Bachelor's, Master's, or PhD in Statistics, Data Science, Computer Science, Engineering, Operations Research, or a related technical field; or Equivalent related professional experience.
  • Minimum 1 years hands-on experience in Data Science or Machine Learning roles
  • Minimum 1 years of professional SQL experience, performing advanced queries and optimization techniques
  • Proficient coding skills in Python, with experience writing clean, maintainable, and optimized ML code
  • Experience applying statistical and machine learning algorithms, such as regression, decision trees, clustering, neural networks, survival analysis, along with model evaluation techniques
  • Strong communication and collaboration skills, with the ability to work both independently and as part of a team

Nice to have

  • Experience developing and deploying machine learning models in production environments
  • Familiarity with machine learning libraries/frameworks such as Scikit-learn, TensorFlow, and PyTorch
  • Exposure to cloud-based ML infrastructure and data pipelines (e.g., AWS, GCP, Azure)
  • Knowledge and experience in retail

What the JD emphasized

  • production-ready
  • production environments

Other signals

  • building predictive models
  • developing algorithms
  • demand forecasting
  • assortment
  • inventory positioning
  • pricing optimization
  • deploying machine learning models in production environments