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Actuarial Data Analyst Jobs (NOW HIRING)

Perform actuarial data analysis and prepare inputs for valuation and reporting processes Conduct actuarial calculations, including more complex modelling and analysis where needed. * Support the ...

NY, CO, CA) There are a number of Actuarial roles at various levels available with a growing P&C ... Analyze detailed claims data to detect trends and aberrations by TPA, line of business, class, etc.

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Actuarial Data Analyst information

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$22K

$93.5K

$154K

How much do actuarial data analyst jobs pay per year?

As of Sep 10, 2026, the average yearly pay for actuarial data analyst in the United States is $93,525.00, according to ZipRecruiter salary data. Most workers in this role earn between $71,500.00 and $109,500.00 per year, depending on experience, location, and employer.

What is an actuarial data analyst?

Actuarial Data Analysts are professionals who use statistical and mathematical methods to analyze data for assessing financial risks, often in the insurance and finance industries. They collect, organize, and interpret large sets of data to help actuaries and organizations make informed decisions about pricing, reserving, and risk management. Their work supports the development of financial models, forecasts, and reports, ensuring accuracy in predicting future events that impact business operations. Actuarial Data Analysts often work closely with actuaries, data scientists, and other analysts to deliver actionable insights.

What are the key skills and qualifications needed to thrive as an actuarial data analyst?

To thrive as an Actuarial Data Analyst, you need strong quantitative skills, a background in mathematics or statistics, and often a relevant bachelor’s degree. Proficiency with statistical software (such as SAS, R, or Python), Excel, and sometimes actuarial exam progress or certifications is expected. Analytical thinking, attention to detail, and effective communication help you interpret data and convey complex findings clearly to stakeholders. These skills enable accurate risk assessment, data-driven decision-making, and support for insurance or finance operations.

What are some common challenges faced by actuarial data analysts in managing large datasets?

Actuarial Data Analysts often work with complex and extensive datasets, which can pose challenges such as ensuring data accuracy, dealing with incomplete or inconsistent information, and efficiently processing large volumes of data. Staying organized and using advanced statistical software helps mitigate these issues, but analysts must also regularly collaborate with IT and other teams to resolve data integrity concerns. Developing strong data validation and cleaning processes is key to delivering accurate actuarial models and analyses.

What is the difference between Actuarial Data Analyst vs Actuary?

AspectActuarial Data AnalystActuary
CredentialsBachelor's degree, possibly actuarial examsBachelor's degree, multiple actuarial exams, professional certification (e.g., ASA, FSA)
Work EnvironmentData analysis, reporting, supporting actuarial teamsPricing, reserving, risk assessment, strategic decision-making
Industry UsageInsurance companies, consulting firms, financial servicesInsurance, pension funds, consulting, risk management

The main difference is that Actuarial Data Analysts focus on data processing and supporting actuarial functions, often with fewer certifications, while Actuaries perform complex risk assessments and strategic decisions with advanced credentials. Both roles work closely within insurance and financial industries but differ in responsibilities and certification requirements.

Can an actuarial data analyst become a data analyst?

An actuarial data analyst can transition to a data analyst role since both positions involve analyzing data, using tools like Excel, SQL, and statistical software. However, a data analyst may need to develop additional skills in programming languages such as Python or R and gain experience with broader data visualization and business intelligence tools. Certifications like the Microsoft Certified Data Analyst or courses in data science can facilitate this transition.

What does an actuarial data analyst do?

An actuarial data analyst collects, analyzes, and interprets data related to insurance, finance, or risk management to support decision-making. They use statistical tools and software such as Excel, SQL, or R, and often work closely with actuaries to develop models, assess risk, and improve pricing strategies.
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Infographic showing various Actuarial Data Analyst job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 83% Full Time, 12% Part Time, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $93,525 per year, or $45 per hour.

Actuarial Data Scientist

Pelham, NY • On-site

$120K - $190K/yr

Other

Medical, Dental, Vision, Retirement, PTO

Posted 15 days ago


Job description

Overview

Good Things Start Here.

Good things are happening at Berkshire Hathaway GUARD Insurance Companies—an A+ (Superior) rated, nationwide Property & Casualty insurer backed by Berkshire Hathaway. With supportive leadership, collaborative teams, and opportunities to grow, GUARD is a place where people build meaningful, long‑term careers.

Good Things You Can Count On.

  • Hybrid schedule: 2 days remote / 3 in‑office
  • Competitive pay + generous PTO
  • Medical, dental & vision starting day one
  • 401(k), tuition reimbursement & longevity bonuses
Responsibilities

We're seeking an Actuarial Data Scientist that would be reporting to the AVP, Data Science & Commercial Lines Pricing. Thisrole combines actuarial analytics, predictive modeling, and technical leadership to advance pricing sophistication, risk segmentation, underwriting performance, and profitability. The successful candidate will help modernize actuarial modeling practices, develop scalable analytical solutions, and promote the adoption of data science and software engineering best practices throughout the organization.

Day-to-day:

  • Partner with actuarial to develop analytical solutions that improve pricing sophistication, risk segmentation, underwriting performance, and profitability.
  • Develop and enhance pricing, segmentation, profitability, and risk selection models across Commercial Lines products using both actuarial methodologies and modern machine learning techniques.
  • Apply advanced analytics to pricing, underwriting, claims, and other insurance datasets to identify trends, emerging risks, and opportunities for profitable growth.
  • Support rate reviews, indication analyses, profitability studies, portfolio management, and other pricing initiatives through advanced analytical techniques.
  • Develop predictive models using GLMs and other statistical and machine learning approaches while balancing predictive performance, business value, interpretability, and regulatory considerations.
  • Research and evaluate internal and external data sources to enhance underwriting, pricing, and portfolio insights.
  • Build scalable, production-ready analytical workflows and collaborate with data engineering and technology teams to operationalize models and analytical solutions.
  • Promote best practices in model development, coding standards, testing, documentation, version control, reproducibility, model governance, and performance monitoring.
  • Provide technical guidance and mentorship to actuarial and analytical teams, helping advance the adoption of modern data science and software development practices.
  • Communicate analytical findings and recommendations to technical and non-technical audiences, including senior leadership, and help drive adoption of analytical solutions across the organization.
Qualifications

Required

  • Bachelor's degree in Actuarial Science, Statistics, Mathematics, Data Science, Computer Science, Engineering, Economics, or a related quantitative field.
  • 3-5 years of experience in Actuarial Data Science field
  • Experience developing predictive models and advanced analytical solutions in a business environment.
  • Strong proficiency in Python, including development of production-quality analytical code.
  • Advanced SQL skills for large-scale data extraction, transformation, and analysis.
  • Experience working with large, complex datasets and statistical modeling techniques.
  • Strong communication, collaboration, problem-solving, and stakeholder management skills.
  • Ability to work independently in a fast-paced environment.

Preferred

  • Experience working with actuarial pricing methodologies.
  • Experience with Commercial Lines products such as Workers Compensation, Commercial Auto, General Liability, Businessowners (BOP), Professional Liability, Umbrella, or similar coverages.
  • Experience leading actuarial or analytical modernization initiatives.
  • Familiarity with Git or Azure DevOps, code review processes, CI/CD concepts, package management, and collaborative development workflows.
  • Familiarity with model governance, validation, and monitoring frameworks.
  • Candidates with combined actuarial and data science backgrounds are strongly encouraged to apply.

Applicants must be authorized to work in the U.S. without current or future sponsorship

Salary Range:

$120,000 – $190,000.

In addition to base salary, this role will be eligible for a short-term incentive plan (performance-based bonus), subject to individual and company performance.

The annual base salary range posted represents a broad range of salaries around the U.S. and is subject to many factors including but not limited to credentials, education, experience, geographic location, job responsibilities, performance, skills and/or training.

The successful candidate is expected to work in our NYC office 3 days per week and also be available for travel as required.

Interview Integrity Notice: Berkshire Hathaway GUARD is committed to a fair and consistent hiring process. Candidates are expected to participate independently in interviews. Unauthorized recording, transcription, AI note-taking, or AI interview assistance tools may not be used during interviews without prior approval.

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