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AI Warehouse Slotting Optimization Analyst

Miles Andersen

Full-time · Job · Chicago

About the role

Where you put a product in a warehouse determines how fast you can pick it. High-velocity items should be at eye level near the packing station. Heavy items should be on lower shelves. Products frequently ordered together should be in adjacent slots. Getting slotting right reduces pick times by 30% — which translates to millions in labor savings for warehouses processing 50,000 orders per day. Our slotting optimizer uses order history, product dimensions, pick frequency patterns, and seasonal demand forecasts to recommend optimal product placement. You'll build the analysis pipeline: computing pick frequency statistics, identifying co-pick patterns, modeling the impact of placement changes on pick path efficiency, and producing reslotting recommendations that warehouse managers can execute during low-traffic shifts. The warehouse intelligence team is five people. You'll work with an operations consultant who validates recommendations against real-world constraints that the data doesn't capture — like which aisles can handle forklift traffic and which shelves can support heavy items.

Responsibilities

  • Build analysis pipelines computing pick frequency and co-pick patterns
  • Model the impact of product placement changes on pick path efficiency
  • Generate reslotting recommendations optimized for order fulfillment speed
  • Account for physical constraints: weight limits, aisle width, equipment access
  • Validate recommendations against actual pick time improvements
  • Present optimization proposals to warehouse operations managers

Requirements

  • 3+ years in data analysis or operations research
  • Strong Python and SQL skills for data processing
  • Understanding of optimization concepts and constraint modeling
  • Experience working with operational teams on data-driven recommendations
  • Interest in logistics, warehousing, or supply chain optimization

Benefits

  • Fully remote with flexible schedule
  • Health and dental insurance
  • $2,500 professional development fund
  • Quarterly team meetups
  • Direct impact on warehouse efficiency metrics

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Categories

data science & analytics Automation & AI Workflows

Skills

python sql statistics pandas docker

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$80k – $100k / year

AI Expertise

data science & analytics Automation & AI Workflows

Skills

python sql statistics pandas docker aws

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