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Insurance Data Analytics Jobs (NOW HIRING)

Porch.com, Inc.

Insurance Data Analyst

Seattle, WA

$88K - $123K/yr

Insurance Data Analyst Location: United States Workplace Type: Remote About the Job The future is ... analytics, or related roles. * Strong SQL skills with hands-on experience in BigQuery and/or SQL ...

Porch.com, Inc.

Insurance Data Analyst

Seattle, WA · On-site +1

$88K - $123K/yr

Insurance Data Analyst Location: United States Workplace Type: Remote About the Job The future is ... analytics, or related roles. * Strong SQL skills with hands-on experience in BigQuery and/or SQL ...

1 point system

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 ...

Everest

AVP, Data & Analytics Product Owner

Warren, NJ · On-site

Responsibilities : • Provide strategic product ownership for Insurance data and analytics, with end‑to‑end accountability for product vision, roadmap, prioritization, delivery, adoption, and ...

Orion180

Senior Insurance Data Analyst

Irving, TX · On-site

$82K - $104K/yr

JOIN OUR TEAM AS A SENIOR INSURANCE DATA ANALYST ... We believe analytics isn't a back-office function. It's not a reporting layer bolted onto the ...

Everest

AVP, Data & Analytics Product Owner

Warren, NJ · Hybrid

$201K - $225K/yr

Provide strategic product ownership for Insurance data and analytics, with endtoend accountability for product vision, roadmap, prioritization, delivery, adoption, and ongoing value realization.

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 ...

Oscar Health

Associate, Provider Data Analytics

Dallas, TX · Remote

$87K - $114K/yr

Oscar is the first health insurance company built around a full stack technology platform and a ... The Associate, Provider Data Analytics reviews provider data-related issues and scales solutions ...

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Insurance Data Analytics information

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How much do insurance data analytics jobs pay per hour?

As of Jun 16, 2026, the average hourly pay for 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 are the key skills and qualifications needed to thrive in the Insurance Data Analytics position, and why are they important?

To thrive in Insurance Data Analytics, you need a solid understanding of data analysis, statistics, and insurance industry concepts, usually supported by a degree in mathematics, statistics, finance, or a related field. Proficiency with analytical tools like SQL, Python, R, and data visualization platforms (such as Tableau or Power BI), as well as certifications like CPCU or advanced analytics credentials, are highly valued. Strong problem-solving abilities, attention to detail, and effective communication skills help analysts translate complex data into actionable business insights. These skills are crucial for driving informed decision-making, risk assessment, and operational improvements within insurance organizations.

What are the typical responsibilities of someone working in Insurance Data Analytics?

Professionals in Insurance Data Analytics are responsible for collecting, cleaning, and analyzing large sets of insurance-related data to identify trends, assess risk, and inform business decisions. They commonly develop predictive models, generate reports, and provide actionable insights that help underwriting teams, actuarial staff, and business leaders optimize processes or pricing strategies. Day-to-day tasks may also include collaborating with IT and business units to define data requirements, presenting findings to non-technical stakeholders, and ensuring data integrity. This role often involves a mix of independent analysis and team-oriented projects, offering a dynamic and engaging work environment for problem solvers.

How is data analytics used in insurance?

Insurance Data Analytics involves analyzing large datasets to assess risk, set premiums, detect fraud, and improve underwriting accuracy. Data analysts in this field use statistical tools and machine learning techniques to inform decision-making and optimize insurance operations.

What does a data analyst do in insurance?

An insurance data analyst collects, processes, and analyzes insurance data to identify trends, assess risks, and support decision-making. They use tools like Excel, SQL, and data visualization software to create reports and models that improve underwriting, claims management, and pricing strategies.

Is 40 too late for data science?

For an Insurance Data Analytics role, starting a career in data science at age 40 is feasible, as the field values skills, experience, and continuous learning over age. Many professionals transition into data analytics later in their careers by acquiring relevant skills such as programming, statistics, and tools like SQL or Python, often through online courses or certifications.

Is AI replacing data analysts?

Insurance Data Analysts use AI tools to enhance data processing and insights, but AI is not replacing the role entirely. Instead, it automates routine tasks, allowing analysts to focus on complex analysis, strategy, and decision-making that require human judgment and domain expertise.

What is an Insurance Data Analytics job?

An Insurance Data Analytics job involves analyzing large volumes of insurance-related data to identify trends, assess risks, detect fraud, and improve decision-making. Professionals in this field use statistical models, machine learning, and data visualization tools to extract insights that help insurers optimize pricing, enhance customer experience, and reduce losses. They work with claims data, policyholder information, and external data sources to drive business strategy. Strong analytical skills, proficiency in data tools like SQL, Python, or R, and knowledge of insurance principles are essential for success in this role.

More about Insurance Data Analytics jobs
What cities are hiring for Insurance Data Analytics jobs? Cities with the most 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 Insurance Data Analytics jobs? States with the most job openings for Insurance Data Analytics jobs include:
What job categories do people searching Insurance Data Analytics jobs look for? The top searched job categories for Insurance Data Analytics jobs are:
Insurance Data Analytics jobs near you
Infographic showing various Insurance Data Analytics job openings in the United States as of June 2026, with employment types broken down into 92% Full Time, and 8% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% 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

Posted 2 days ago

Job description

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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