Senior Cargo Claims Examiner (English)

You will review air cargo and parcel delivery claims to support the training of AI claims systems. The work involves careful judgment based on written records, photographs, and freight documents.

• Review air waybills, shipment photographs, delivery records, and related freight documents.
• Assess custody, liability, and compliance with release rules for disputed shipments.
• Evaluate delivery photo disputes using visual and written evidence.
• Write concise rationales that support custody and liability decisions.
• Find inconsistencies, missing information, and procedural issues in claims submissions and freight records.
• Provide expert input to improve AI-powered claims processes.

This is remote, part-time contract work for an experienced cargo or parcel claims professional. English fluency is required.

• Pay: $20 to $60 per hour.
• Time: Less than 20 hours per week.
• Work arrangement: Fully remote, part-time contract work.
• Location: Open to candidates in the countries specified for this role across the Americas, Europe, Asia, Africa, and the Middle East.
• Language: Fluent in English.
• Experience: At least five years in air cargo, ground handling, or parcel carrier claims examination or dispute resolution.
• Knowledge: Custody determination, release rules, and liability assessment in air and ground freight contexts.
• Skills: Freight document review, photographic evidence review, parcel delivery dispute handling, clear communication, careful attention to detail, precise reasoning, independent work, and the ability to meet project requirements.
• Helpful background: Deciding claim outcomes, approving or denying claims, apportioning liability, supporting claims audits or process improvements, and handling airline air cargo matters.
• Additional experience that may help: Air waybills, ULD handling, the Montreal Convention, or IATA cargo training.

Apply on OpenTrain with your resume, then complete the application on the hiring site.

AI training is the human work behind systems that learn from examples. Experienced professionals review real-world material, apply sound judgment, and explain decisions so AI systems can handle similar tasks more accurately.

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