Commercial Senior Auto Claims Adjuster- Remote

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Job Title

Commercial Senior Auto Claims Adjuster- Remote

Requisition Number

R7308 Commercial Senior Auto Claims Adjuster- Remote (Open)

Location

Nevada - Home Teleworkers

Additional Locations

Arizona - Home Teleworkers, Arizona - Home Teleworkers, Arkansas - Home Teleworkers, California - Home Teleworkers, Colorado - Home Teleworkers, Connecticut - Home Teleworkers, Delaware - Home Teleworker, District of Columbia - Home Teleworkers, Florida - Home Teleworkers, Georgia - Home Teleworkers, Idaho - Home Teleworkers, Illinois - Home Teleworkers, Indiana - Home Teleworkers, Iowa - Home Teleworkers, Kansas - Home Teleworker, Kentucky - Home Teleworkers, Louisiana - Home Teleworkers, Maine Home Teleworkers, Maryland - Home Teleworkers, Massachusetts - Home Teleworkers, Michigan - Home Teleworkers, Minnesota - Home Teleworkers, Mississippi - Home Teleworker, Missouri - Home Teleworker, Montana - Home Teleworkers {+ 19 more}

Job Information

We're Mobilitas, a commercial insurance company created by CSAA Insurance. Our mission is to reinvent commercial insurance in the mobility space by providing technologically advanced solutions for today’s way of doing business. At Mobilitas, we believe in what's possible, we use our inventive skills to create new opportunities - we're not chasing the status quo, we're chasing a shared vision. We’re looking for motivated, innovative individuals who think big, move fast and are dedicated to creating a company from the ground up, without the constraints of a traditional insurance company. We’re excited to push the boundaries of commercial insurance and are looking for enthusiastic team members to help us reimagine insurance.

Hablas Español? Estamos buscando agentes de reclamaciones bilingües!

Your role:

Our Commercial Senior Auto Claims Adjusters’ responsibilities include handling non-injury claims for commercial accounts and personal lines non-standard products.

Your work:

• Manage a large inventory of claims ranging from minor to moderate complexity.

• Answer inbound calls, open claims, acquire loss details from all involved parties and:

• Investigate, evaluate, negotiate, and settle auto and property damage claims for commercial rideshare, commercial GIG economy and/or personal lines non-standard products.

• Explain policy, coverage, and appropriate course of action.

• Arrange and pay repair, rental, towing, total losses and negotiate storage.

• Document claim files, send correspondence, and communicate with all parties involved.

• Work from a diary system to review and resolve claims within state compliance guidelines, policy provisions and standard operating procedures.

Training Schedule:

• Paid virtual training starts on September 15th and consists of a combination of classroom and hands on training. The training schedule is Monday through Friday from 8 a.m. to 5 p.m. pacific time.

Required Experience, Education and Skills:

• High School Diploma/GED or 2+ years of customer service experience in a virtual environment leveraging internet, phone, and chat features.

• 1-2+ years of auto or commercial adjusting/liability experience required.

• Must hold an Adjuster licensed for all applicable states or obtain license(s) within 90 days of filling position as a condition of employment.

What would make us excited about you?

• 3+ years of prior claims adjusting experience preferred.

• Auto physical damage training and/or I-CAR or SCLA enrollment preferred.

• Bilingual a plus.

• Empathy, Adaptability, Time Management, Organization.

• Flexibility to support Pacific Time operating hours of 5:30am to 7:00pm. Ability to support weekend shifts as needed. Flexibility to work common shifts of 9:00am-6:00pm or 10:00am-7:00pm in most time zones.

• Capability to complete training and perform the job in a remote setting as required.

• Strong computer navigation skills.

• Outstanding interpersonal skills.

• Ability to handle several tasks simultaneously.

• Shows respect for differences through excellent communication skills with people from an array of backgrounds.