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Insurance Data Analytics Jobs (NOW HIRING)

BizTek People

Insurance Data Analyst

Portland, OR · On-site

Insurance Data Analyst 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 ...

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 ...

Georgia IT Inc

Insurance Data Analyst

Manhattan, NY · On-site

Data Analyst With Insurance Domain Location: NYC, NY Duration: Long Term Skills Required: * Detailed knowledge of commercial insurance underwriting process in both for property and liability lines

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 ...

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 ...

Oscar Health

Associate, Provider Data Analytics

Tempe, AZ · Hybrid

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

As of Jul 7, 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?

In insurance, data analytics is used by professionals to assess risk, set premiums, detect fraud, and improve customer segmentation. Analysts utilize tools like statistical models and machine learning algorithms to interpret large datasets, enabling more accurate underwriting and claims management. Strong analytical skills and knowledge of data visualization are essential for effective decision-making in this field.

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.

How much does an insurance analyst make?

The average salary for an insurance analyst is around $65,000 to $85,000 per year, depending on experience, location, and industry. Entry-level roles typically start lower, while experienced analysts with specialized skills or certifications can earn higher salaries. Strong analytical skills and proficiency with data tools like Excel or SQL are often required.

Will AI replace a data analyst?

AI can automate routine data processing and analysis tasks, but the role of a data analyst, including those in insurance data analytics, involves interpreting complex data, providing insights, and making strategic decisions that require human judgment. Therefore, AI is more likely to augment rather than fully replace data analysts, who also need skills in data visualization, domain knowledge, and communication. Continuous learning and proficiency with analytics tools remain important for the role.

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 July 2026, with employment types broken down into 1% Internship, 94% Full Time, 3% Part Time, and 2% Contract. Highlights an 79% Physical, 5% Hybrid, and 16% Remote job distribution, with an average salary of $113,873 per year, or $54.7 per hour.
Insurance Data Analyst

Insurance Data Analyst

BizTek People

Portland, OR • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

Insurance Data Analyst

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. This role involves consolidating information from claims, policies, exposures, and historical loss records; performing data quality checks; identifying inconsistencies or gaps; and preparing structured datasets aligned with Riskonnect's data mapping and upload requirements. The contractor will leverage advanced Excel skills—including complex formulas, data cleansing techniques, pivot tables, and data validation tools—to efficiently transform and audit large datasets prior to migration. They will collaborate closely with internal stakeholders and the implementation team to ensure accurate data 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 a fast-moving implementation environment are essential.

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