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Remote Insurance Data Analytics Jobs in New Hope, PA

Amerihealth Caritas

Data & Reporting Analyst Senior

Robbinsville, NJ · On-site +1

$83K - $105K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Remote - DE, NJ, NY, PA must live within 3 hours of Robbinsville NJ * Monday-Friday 8:30am-5:00pm ... insurance, disability insurance, 401(k), paid time off and more . The targeted hiring range for ...

Amerihealth Caritas

Data & Reporting Analyst Sr

Robbinsville, NJ · On-site +1

$83K - $105K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Remote - DE, NJ, NY, PA must live within 3 hours of Robbinsville NJ * Monday-Friday 8:30am-5:00pm ... insurance, disability insurance, 401(k), paid time off and more . The targeted hiring range for ...

ParetoHealth

Data Analyst

Philadelphia, PA · Remote

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

As a Data Analyst, you will partner closely with Data Scientists and cross-functional stakeholders ... analytics role, preferably in the health benefits space/insurance industry, with a solid ...

ParetoHealth

Lead Data Analyst

Philadelphia, PA · Remote

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

... based analytics role, preferably in the health benefits space/insurance industry * Working knowledge of Python for analysis and data wrangling (advanced modeling not required) * Deep, hands-on ...

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All JobsInsurance Data Analytics JobsRemote Insurance Data Analytics JobsRemote Insurance Data Analytics Jobs in New Hope, PA

Remote Insurance Data Analytics information

See New Hope, PA salary details

$24

$53

$92

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

As of Aug 17, 2026, the average hourly pay for remote insurance data analytics in New Hope, PA is $53.72, according to ZipRecruiter salary data. Most workers in this role earn between $43.17 and $60.87 per hour, depending on experience, location, and employer.

What is remote insurance data analytics?

Remote insurance data analytics is the practice of analyzing insurance-related data, such as claims, risk assessments, and customer information, from a location outside of a traditional office setting. Professionals in this field use statistical methods, data mining, and machine learning tools to identify patterns, detect fraud, and help insurance companies make data-driven decisions. This remote role often requires proficiency in data analysis tools like SQL, Python, or R, and a strong understanding of insurance industry concepts. Remote insurance data analysts collaborate with teams virtually to provide insights and support business strategies, making it a flexible career option.

What are the key skills and qualifications needed to thrive as a remote insurance data analytics professional?

To excel in Remote Insurance Data Analytics, you need strong analytical skills, a background in statistics or mathematics, and typically a degree in data science, actuarial science, or a related field. Familiarity with data analysis tools like SQL, Python, R, and specialized insurance analytics platforms such as SAS or Tableau, as well as relevant certifications, is highly valuable. Attention to detail, problem-solving abilities, and effective communication set candidates apart in this role. These skills are crucial for transforming complex insurance data into actionable insights that drive informed business decisions and risk assessments.

How do remote insurance data analytics professionals typically collaborate with cross-functional teams to drive business insights?

Remote Insurance Data Analytics professionals often work closely with underwriters, actuaries, claims managers, and IT teams to gather data requirements, interpret findings, and implement data-driven solutions. Collaboration usually happens through virtual meetings, collaborative dashboards, and project management tools to ensure clear communication and alignment on objectives. This cross-functional approach helps identify trends, optimize risk assessments, and support strategic decision-making within the organization. Building strong relationships with team members across departments is key to successfully translating analytical results into actionable business strategies.

What is the difference between Remote Insurance Data Analytics vs Remote Insurance Underwriter?

AspectRemote Insurance Data AnalyticsRemote Insurance Underwriter
Required CredentialsBachelor's in Data Science, Statistics, or related field; often certifications in data analysis or analyticsBachelor's in Business, Finance, or related; often requires insurance licensing or certifications
Work EnvironmentPrimarily data analysis, modeling, and reporting; often collaborative with IT and actuarial teamsAssessing risks, reviewing applications, making underwriting decisions; involves communication with agents and clients
Employer & Industry UsageUsed across insurance companies, reinsurers, and brokers for data-driven decision makingUsed by insurance carriers to evaluate and approve policies

Remote Insurance Data Analytics focuses on analyzing insurance data to inform business decisions, while Remote Insurance Underwriters evaluate individual insurance applications to determine coverage. Both roles are essential in the insurance industry but differ in daily tasks and required skills.

What are the most commonly searched types of Insurance Data Analytics jobs in New Hope, PA?

The most popular types of Insurance Data Analytics jobs in New Hope, PA are:

What cities near New Hope, PA are hiring for Remote Insurance Data Analytics jobs?

Cities near New Hope, PA with the most Remote Insurance Data Analytics job openings:

Remote Insurance Data Analytics jobs near you
Infographic showing various Remote Insurance Data Analytics job openings in New Hope, PA as of June 2026, with employment types broken down into 82% Full Time, 8% Part Time, and 10% Contract. Highlights an 85% Physical, 2% Hybrid, and 13% Remote job distribution, with an average salary of $111,747 per year, or $53.7 per hour.

Data Analytics Developer (Remote Eligible)

Mathematica

Princeton, NJ • On-site, Remote

Full-time

Re-posted 8 days ago

Job description

Mathematica applies expertise at the intersection of data, methods, policy, and practice to improve well-being around the world. We collaborate closely with public- and private-sector partners to translate big questions into deep insights that improve programs, refine strategies, and enhance understanding. Our work yields actionable information to guide decisions in wide-ranging policy areas, from health, education, early childhood, and family support to nutrition, employment, disability, and international development. Mathematica offers our employees competitive salaries, and a comprehensive benefits package, as well as the advantages of being 100 percent employee owned. As an employee stock owner, you will experience financial benefits of ESOP holdings that have increased in tandem with the company's growth and financial strength. You will also be part of an independent, employee-owned firm that is able to define and further our mission, enhance our quality and accountability, and steadily grow our financial strength. Learn more about our benefits here: https://www.mathematica.org/career-opportunities/benefits-at-a-glance

At Mathematica, we take pride in our commitment to diversity. Building an inclusive culture that draws on the individual strengths of employees from different ethnic backgrounds, cultures, lifestyles, abilities, and experience is key to our success.

We are seeking an intellectually curious Data Analytics Developer in any of our locations who is passionate about using big data to answer research questions that influence decision making in U.S. health care policy. The ideal candidate will work in languages such as Python and SQL. Individuals with an interest in confronting the challenges of working with large, complex data sets, an interest in data manipulation and analysis, and a desire to become a subject matter expert in health care data are strongly encouraged to apply.

Responsibilities:

  • Working with large secondary data sources such as Medicare and Medicaid administrative claims data and survey data to effectively answer research questions about health care policy
  • Providing creating and best practice solutions for data processing optimization, commonly on cloud computing platforms
  • Ensuring data quality through all stages of data processing with rigorous quality control
  • Iteratively informing requirements and specifications, which will include articulating findings and working with researchers to adjust specifications and code as interim results are found 

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