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AI Cross-Docking Network Optimizer

Miles Andersen

Full-time · Job · Chicago

About the role

Cross-docking is the logistics practice where freight arriving at a terminal is immediately sorted and routed outbound without entering storage — reducing warehouse handling cost and transit time. Done well, it's one of the highest-value operations in a regional distribution network. Done poorly, it creates bottlenecks where freight misses outbound connections and dwell time destroys any efficiency gain. We optimize regional logistics networks for 25 carriers and 3PLs. Cross-docking decisions — which freight to cross-dock vs. store, which outbound connection to target, which dock door to assign — are currently made by experienced dock supervisors using intuition and experience. The optimization opportunity is significant: even 15% improvement in freight-to-connection matching reduces transit time by an average of 4 hours per shipment. You'll build the cross-docking optimization models: learning from historical freight flows and connection performance data to recommend real-time cross-dock decisions, predicting outbound connection punctuality to inform timing decisions, and integrating with dock management systems to surface recommendations to supervisors. The supervisor keeps control; your models improve the quality of information they act on.

Responsibilities

  • Build freight-to-connection matching models for real-time cross-docking decisions
  • Develop outbound connection punctuality prediction incorporating schedule, traffic, and historical performance data
  • Design dock door assignment optimization balancing throughput and proximity
  • Integrate optimization recommendations into dock management system APIs
  • Create supervisor-facing explanation of recommendations highlighting key confidence factors
  • Monitor decision quality using realized transit time outcomes versus alternatives

Requirements

  • 3+ years in logistics analytics, operations research, or transportation data science
  • Strong Python and SQL skills with optimization problem modeling experience
  • Familiarity with transportation or logistics network concepts
  • Experience building decision support systems with real-time data integration
  • Ability to validate model recommendations against operational outcomes

Benefits

  • Fully remote with flexible schedule
  • Health and dental insurance
  • $2,500 annual professional development budget
  • 401(k) with match
  • Performance bonus tied to network efficiency metrics

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Categories

Automation & AI Workflows data science & analytics

Skills

python sql pandas spark statistics

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$80k – $100k / year

AI Expertise

Automation & AI Workflows data science & analytics

Skills

python sql pandas spark statistics scikit-learn

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