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Data Scientist - Merchandising And Inventory Machine Learning

Job in Seattle, King County, Washington, 98127, USA
Listing for: brobstongroup.com - Jobboard
Full Time position
Listed on 2026-06-02
Job specializations:
  • IT/Tech
    Machine Learning/ ML Engineer, Data Scientist, AI Engineer, Data Engineer
Salary/Wage Range or Industry Benchmark: 80000 - 100000 USD Yearly USD 80000.00 100000.00 YEAR
Job Description & How to Apply Below

Summary

Perform machine learning and optimization to solve merchandising and inventory challenges, building predictive models and scalable algorithms to support demand forecasting, assortment, inventory positioning and pricing optimization. Collaborate with cross functional teams to design, implement and deploy production ready solutions and communicate insights to technical and non technical stakeholders.

Responsibilities
  • Analyze large datasets and perform exploratory data analysis and statistical modeling
  • Design develop and implement scalable machine learning models and optimization algorithms
  • Build and maintain data pipelines to process clean and transform data from diverse sources
  • Implement evaluation and monitoring to validate model performance and reliability
  • Partner with product managers engineers and business partners to identify opportunities for ML applications
  • Deploy production ready solutions and ensure maintainability of model code
  • Communicate complex findings insights and trade offs to stakeholders
Requirements
  • Bachelor s, Master s or PhD in a quantitative field or equivalent experience
  • Minimum 1 years hands on experience in data science or machine learning roles
  • Minimum 1 years professional SQL experience with advanced queries and optimization
  • Proficient coding skills in Python and experience writing clean maintainable ML code
  • Experience with statistical and machine learning algorithms and model evaluation techniques
  • Familiarity with ML libraries such as Scikit learn, Tensor Flow or Py Torch
  • Exposure to cloud based ML infrastructure and data pipelines such as AWS, GCP or Azure
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