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Insurance Data Analytics Jobs in Oregon (NOW HIRING)

Origami Risk LLC

(Senior) Corporate Data Analyst

$67K - $101K/yr

Preferred Experience with Databricks, Thoughtspot, , and other data lake / warehouse and analytics ... Fully Paid by Origami Risk - Vision insurance, Short & Long-Term Disability Insurance, and Basic ...

Formfactor

Senior Data Analyst - Financial Analytics

OR · On-site +1

$132K - $173K/yr

This role will leverage best practices in data modeling, visualization, and analytics to deliver ... We offer a full benefits package, including medical, dental, vision, life insurance, disability ...

360training

Head of Analytics

Leverage data and information to fundamentally alter outcomes for the business * Help evaluate and ... Insurance- Basic, Voluntary, and AD amp;D • Healthcare- Medical, Dental, Vision, and MDLive • ...

Care Access

Enterprise Performance Analytics Engineer

OR · Remote

$80K - $110K/yr

Transform data into well-documented, tested, and trusted datasets for analysis and reporting ... insurance plan options * Health Savings Account and Flexible Spending Accounts * Bi-weekly HSA ...

Natera

Market Access Payor Analyst

OR · Remote

$20 - $27.25/hr

Using data analytics, they derive logical conclusions and forecasts from implementing strategies ... Researches complex benefits and insurance verification using various systems and portals internal ...

Prodege LLC

OR

$85K - $100K/yr

The Sr. Analyst, Data Visualization role supports the delivery of one of our most impactful ... insurance. Employees receive flexible PTO, as well as paid sick leave prorated based on hire date.

Ascension

Technical Analyst Data and Systems

$96K - $134K/yr

... support downstream analytics. * Conduct detailed data flow analysis to map source-to-target ... Benefits Paid time off (PTO)Various health insurance options & wellness plansRetirement benefits ...

UMB Financial

Sr. Fraud BI Analyst

OR · On-site +1

$98K - $144K/yr

Bachelor's Degree in Data Analytics, Data Science or similar field of study OR equivalent ... insurance coverage; health savings, flexible spending, and dependent care accounts; adoption ...

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Insurance Data Analytics information

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.

What are the most commonly searched types of Insurance Data Analytics jobs in Oregon? The most popular types of Insurance Data Analytics jobs in Oregon are:
What are popular job titles related to Insurance Data Analytics jobs in Oregon? For Insurance Data Analytics jobs in Oregon, the most frequently searched job titles are:
What job categories do people searching Insurance Data Analytics jobs in Oregon look for? The top searched job categories for Insurance Data Analytics jobs in Oregon are:
Insurance Data Analytics jobs near you
Infographic showing various Insurance Data Analytics job openings in Oregon as of July 2026, with employment types broken down into 1% Internship, 92% Full Time, 5% Part Time, and 2% Contract. Highlights an 79% Physical, 5% Hybrid, and 16% Remote job distribution.

Insurance Data Analyst

BizTek People, Inc. | APA International Placement Consultants

Portland, OR • On-site

Contractor

Posted 12 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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