Claims Handler - Property

Location: Dallas, TX (hybrid) Hybrid| Full-time Compensation: $50,000 - $75,000 We are hiring on behalf of our client, an innovative, fast-growing AI-enabled Third-Party Administrator (TPA) is seeking a Property Claims Handler to join its team in Dallas, TX. Operating on a hybrid schedule, this role is designed for a target-driven professional with 3+ years of property claims experience who thrives in a high-performance, merit-based environment. The client provides end-to-end insurance claims management for over 50 insurers and Managing General Agents (MGAs). Supported by top-tier venture backing and state-of-the-art technology, the organization combines cutting-edge AI tools with an expert operational team. This position offers real responsibility, robust professional development—spanning capacity planning, data, quality management, and leadership—and an accelerated career track designed to build the industry's future claims leaders. Key Responsibilities Handle property claims end-to-end, managing the full process from first notification of loss (FNOL) through to final settlement. Communicate directly with policyholders throughout the claims journey to ensure a clear, transparent, and positive customer experience. Review policy documentation, verify coverage details, and accurately assess liability. Establish appropriate reserve levels and effectively manage claims within designated settlement authority limit levels. Identify potential fraud indicators and elevate suspicious claims following established protocols. Maintain complete, accurate, and audit-ready claims records to uphold regulatory compliance. Coordinate with third-party vendors, loss adjusters, forensic experts, and secondary service providers as needed. Identify operational inefficiencies and contribute to process improvements within the claims workflow. Location & Eligibility: Based in the Dallas, TX area with valid US work authorization (Hybrid work arrangement). Experience: 3+ years of property claims handling experience (including commercial, personal property, buildings, contents, or renters). Licensing: Active Texas (TX) Adjuster License is required. Additional licenses in CA, FL, GA, or NY are highly advantageous. Core Competencies: Strong organizational abilities, self-directed prioritization, and a comfort with utilizing data to inform decision-making. Additional Assets: Prior experience in subrogation, mentorship/team leadership, or background within a startup or high-growth environment. Competitive Base Salary: $50,000 – $75,000 per annum. Performance Bonus: Up to 15% quarterly performance-based bonus. Equity: Stock option participation as an early team member. Healthcare: Comprehensive medical insurance coverage. Retirement: 401(k) plan with up to a 5% employer match. Paid Time Off: Generous PTO policy. Career Growth: Fast-track promotion opportunities in a meritocratic, high-growth environment. Commitment to Equality and Accessibility: At MLabs, we are committed to offer equal opportunities to all candidates. We ensure no discrimination, accessible job adverts, and providing information in accessible formats. Our goal is to foster a diverse, inclusive workplace with equal opportunities for all. If you need any reasonable adjustments during any part of the hiring process or you would like to see the job-advert in an accessible format please let us know at the earliest opportunity by emailing View email address on click.appcast.io. MLabs Ltd collects and processes the personal information you provide such as your contact details, work history, resume, and other relevant data for recruitment purposes only. This information is managed securely in accordance with MLabs Ltd’s Privacy Policy and Information Security Policy, and in compliance with applicable data protection laws. Your data may be shared only with clients and trusted partners where necessary for recruitment purposes. You may request the deletion of your data or withdraw your consent at any time by contacting View email address on click.appcast.io. #J-18808-Ljbffr MLabs