Davies

Davies

Property Field Adjuster - Richmond, VA

Home United States

Sponsorship not specifiedDetected 8 days ago
Customer SupportCommunication

About the role

  • Territory Requirement: This position covers the Richmond, VA area and surrounding territories.
  • Candidates must reside within or near the Richmond market and be comfortable traveling throughout the assigned territory to conduct on-site inspections, customer meetings, and claim-related field activities.

Requirements

  • The ideal candidate will have experience managing claims from initial inspection through reporting while maintaining high standards of customer service and technical accuracy.

Company info

  • Imagine being part of a team that's not just shaping the future but actively driving it.
  • At Davies North America, we're at the forefront of innovation and excellence, blending cutting-edge technology with top-tier professional services.
  • As a vital part of the global Davies Group, we help businesses navigate risk, optimize operations, and spearhead transformation in the insurance and regulated sectors.
  • Davies North America is seeking an experienced Property Field Adjuster to support the Richmond, Virginia territory.
  • This role is ideal for a field professional who can investigate losses, inspect sites, document findings, evaluate damages and exposures, and communicate effectively with insureds, claimants, carriers, clients, vendors, and internal stakeholders.
  • The successful candidate will bring strong technical judgment, professional communication skills, and the ability to manage field assignments efficiently and accurately.
  • This position handles residential and commercial property losses, including property inspections, scope review, damage assessment, estimate support, and related file documentation.
  • Compensation: This is a 100% commission‑based role with earnings paid at 42% commission, supported by a bi-weekly draw of $1500.00.

This listing is sourced directly from Davies's careers page and normalized into a canonical job model.