AI Cross-Docking Network Optimizer

Orvion Systems

Remote · $80k – $100k / year · Full-time · Posted 4 months ago

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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.

Fully remote.

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