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Volunteer Insurance Data Analytics Jobs (NOW HIRING)

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Insurance Data Analyst

New York, NY · Remote

Required Qualifications * 5+ years of experience working with insurance bordereaux (BDX) reporting, premium reporting, or insurance data analysis. * Strong understanding of MGA, Program, P&C, or ...

Novella

Insurance Data Scientist

New York, NY · On-site

Hands-on experience with insurance loss data, loss development, exposure analysis, and rate adequacy. * Experience building models that have been deployed in production and used to make real ...

Peak6 Investments LLC

$140 - $190/hr

The Lead, Data Analytics plays a critical role within FOCUS' Group's Data & Technology division ... Personal Days, PTO Days, 12 Paid Holidays, Two Paid Days for Volunteer Service, Paid Parental Leave

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How much do volunteer insurance data analytics jobs pay per hour?

As of Sep 2, 2026, the average hourly pay for volunteer insurance data analytics in the United States is $54.75, according to ZipRecruiter salary data. Most workers in this role earn between $43.99 and $62.02 per hour, depending on experience, location, and employer.

What is the difference between Volunteer Insurance Data Analytics vs Insurance Data Analyst?

AspectVolunteer Insurance Data AnalyticsInsurance Data Analyst
Required CredentialsRelevant certifications, data analysis skills, insurance knowledgeDegree in statistics, data science, or related field; certifications preferred
Work EnvironmentNon-profit, volunteer-based, flexible hoursCorporate insurance companies, structured office settings
Employer & Industry UsageNon-profit organizations, insurance charitiesInsurance firms, financial institutions
Common Search & ComparisonVolunteer Insurance Data Analytics vs Insurance Data Analyst

Volunteer Insurance Data Analytics focuses on analyzing insurance data within volunteer or non-profit contexts, often with flexible or part-time roles. Insurance Data Analysts work in corporate settings, handling large datasets to inform business decisions. Both roles require similar analytical skills and insurance knowledge but differ mainly in work environment and employment type.

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Cities with the most Volunteer Insurance Data Analytics job openings:

What are the most commonly searched types of Insurance Data Analytics jobs?

The most popular types of Insurance Data Analytics jobs are:

What states have the most Volunteer Insurance Data Analytics jobs?

States with the most job openings for Volunteer Insurance Data Analytics jobs include:

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The top searched job categories for Volunteer Insurance Data Analytics jobs are:

Volunteer Insurance Data Analytics jobs near you
Infographic showing various Volunteer Insurance Data Analytics job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 22% Part Time, and 5% Contract. Highlights an 91% Physical, 1% Hybrid, and 8% Remote job distribution, with an average salary of $113,873 per year, or $54.7 per hour.

Insurance Data Analyst

BizTek People, Inc. | APA International Placement Consultants

Portland, OR • On-site

Contractor

Re-posted 20 days ago

Job description


Title: Insurance Data Analyst
Duration: 2 Months
Location: Portland, OR
Job Description
The Insurance Data Analyst contractor will play a key role in supporting our transition to Riskonnect by gathering, validating, and analyzing insurance related data required for system configuration and ongoing reporting.
Requirements
Responsibilities
  • This role involvesconsolidating information from claims, policies, exposures, and historicalloss records; performing data quality checks; identifying inconsistenciesor gaps; and preparing structured datasets aligned with Riskonnect's datamapping and upload requirements.
  • The contractor will leverageadvanced Excel skills-including complex formulas, data cleansingtechniques, pivot tables, and data validation tools-to efficientlytransform and audit large datasets prior to migration.
  • They will collaborate closelywith internal stakeholders and the implementation team to ensure accuratedata migration, support user acceptance testing with analytical insights,and document data processes to enable smooth adoption of the new platform.
  • Clear communication,meticulous attention to detail, and the ability to work independently in afast-moving implementation environment are essential.

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