Nirvana Insurance is hiring a Claims Adjuster - AI Data Collection to revolutionize commercial insurance through data. In this role, you’ll handle commercial auto claims while partnering closely with product, engineering, and data science teams to structure and annotate claim data, directly improving our AI algorithms.

What You'll Do

Analyze coverage based on loss facts and policy language, tagging key data elements for model training.
Make informed liability decisions, weighing comparative negligence, and document reasoning in a structured format.
Collaborate with claims, product, engineering, and data teams on adjustment plans, severity tagging, reserve methodology, and claim best practices.
Study and assess demands and settlements, capturing outcome data in structured formats for system training and model development.
Leverage data from past claims and settlements to detect trends and surface insights that improve AI algorithms.
Work on special projects to assist AI‑powered claims adjusting, such as auditing TPA files, piloting new annotation guides, or beta‑testing internal tooling.
Gather and organize data efficiently using Excel and/or Google Sheets, systematically managing diverse claims datasets.

What We're Looking For

7+ years of experience in commercial auto claim adjudication, with at least 5 years handling injury severity losses.
At least 2 years of commercial carrier claims experience.
Experience handling claims up to $500k FDV.
Active adjuster's license in your home or designated home state.
A consistent track record of exceeding targets, including cycle time and recovery rates.
Strong written and oral communication skills.
A bias-to-action and comfort with stretching yourself outside your comfort zone.
An owner mentality and accountability for your actions and results.
Strong collaboration skills with team members based across the country and the world.
Proven ability to work successfully in high-growth, ambiguous start-up environments.
A college degree or a minimum of 10 years of claims experience.