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Overnight 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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$153K

$178.7K

$202K

How much do overnight insurance data analytics jobs pay per year?

As of Jul 7, 2026, the average yearly pay for overnight insurance data analytics in the United States is $178,749.00, according to ZipRecruiter salary data. Most workers in this role earn between $165,500.00 and $191,500.00 per year, depending on experience, location, and employer.

What are Overnight Insurance Data Analytics?

Overnight Insurance Data Analytics refers to the process of analyzing insurance data during overnight shifts or using automated systems to process large volumes of data outside of regular business hours. This ensures that insurance companies can quickly identify trends, detect fraud, and make informed decisions by the start of the next business day. Professionals in this role typically work with claims data, customer information, and risk assessments using advanced analytical tools and software. The goal is to improve operational efficiency and support decision-making processes.

What unique challenges might I encounter working in an overnight insurance data analytics role?

Working overnight in insurance data analytics can present unique challenges, including adjusting to non-traditional work hours and maintaining effective communication with daytime teams. You may often handle urgent data requests or last-minute reporting, which requires strong problem-solving skills and the ability to work independently. Additionally, you’ll likely need to coordinate with colleagues across different time zones and shifts to ensure seamless handoffs and continuity in analytics projects. Adapting to the overnight schedule while maintaining high attention to detail and data integrity is essential for success in this role.

What is the difference between Overnight Insurance Data Analytics vs Underwriting Analyst?

AspectOvernight Insurance Data AnalyticsUnderwriting Analyst
CredentialsBachelor's in Data Science, Statistics, or related field; certifications like CAP, CPCU beneficialBachelor's in Business, Finance, or related field; certifications like CPCU or ARM advantageous
Work EnvironmentData centers, analytics teams, remote or office settingsInsurance companies, underwriting departments, office settings
Industry UsageFocuses on analyzing insurance data overnight to support decision-makingEvaluates risks and determines policy terms for insurance applications

While both roles involve insurance data, Overnight Insurance Data Analytics primarily focuses on analyzing data during overnight shifts to inform business decisions, whereas Underwriting Analysts assess risks and set policy terms. The roles share similar credentials but differ in daily tasks and work environment.

What are the key skills and qualifications needed to thrive as an Overnight Insurance Data Analytics professional, and why are they important?

To excel in Overnight Insurance Data Analytics, you need strong analytical abilities, proficiency in statistical methods, and a background in mathematics, statistics, or a related field. Familiarity with data analysis tools such as SQL, Python, R, and insurance-specific software is often required, along with experience in using data visualization platforms like Tableau or Power BI. Attention to detail, problem-solving skills, and the ability to communicate findings clearly are essential soft skills for this role. These competencies are vital for accurately interpreting large datasets during non-standard hours, supporting timely business decisions, and identifying trends or anomalies that impact insurance operations.

Is 40 too late for data science?

For an Overnight Insurance Data Analytics role, age is not a barrier; many professionals transition into data science or analytics later in their careers. Success depends on skills, experience, and continuous learning, such as mastering tools like SQL, Python, or R, and gaining relevant certifications. Age should not deter pursuing a data analytics career at 40 or older.

Will AI replace a data analyst?

AI can automate routine data processing and basic analysis tasks, but the role of a data analyst, including an Overnight Insurance Data Analytics professional, involves interpreting complex data, providing insights, and making strategic decisions that require human judgment. AI tools are used to enhance efficiency, but they do not fully replace the need for skilled analysts who understand business context and can communicate findings effectively.

Can a data analyst work at night?

A data analyst, including those working in insurance data analytics, can work night shifts if the employer offers overnight or flexible schedules. Some roles require 24/7 coverage or support for global operations, making night work possible, especially in environments with real-time data monitoring or client support. Skills in remote collaboration tools and time management are helpful for night shift work.

What does a data analyst do in insurance?

A data analyst in insurance collects, processes, and analyzes data related to policies, claims, and customer information to identify trends, assess risks, and support decision-making. They often use tools like Excel, SQL, and data visualization software to create reports and insights that help improve underwriting, pricing, and fraud detection.
What cities are hiring for Overnight Insurance Data Analytics jobs? Cities with the most Overnight 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 Overnight Insurance Data Analytics jobs? States with the most job openings for Overnight Insurance Data Analytics jobs include:
Overnight Insurance Data Analytics jobs near you
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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