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Entry Level Insurance Data Analytics Jobs in Virginia

LHH US

Senior Business Data Analyst

Henrico, VA · Hybrid

$79K - $100K/yr

This role is ideal for an experienced analytics professional who enjoys partnering with business ... life insurance, short-term disability, additional voluntary benefits, EAP program, commuter ...

LHH US

Senior Business Data Analyst

Henrico, VA · Hybrid

$79K - $100K/yr

This role is ideal for an experienced analytics professional who enjoys partnering with business ... life insurance, short-term disability, additional voluntary benefits, EAP program, commuter ...

MANTECH

Associate BI Analytics Engineer

Herndon, VA

... data foundation that drives strategic decision-making across the enterprise. As an entry-level ... Work with business analysts and end-users to understand reporting requirements and document data ...

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All JobsInsurance Data Analytics JobsEntry Level Insurance Data Analytics JobsEntry Level Insurance Data Analytics Jobs in Virginia

Entry Level Insurance Data Analytics information

What is an entry level insurance data analytics job?

Entry level insurance data analytics jobs involve collecting, processing, and analyzing data to help insurance companies make better business decisions. Professionals in these roles typically use statistical tools and software to identify trends, assess risks, and support pricing or policy development. They may also prepare reports and visualizations to communicate findings to other teams. These positions are ideal for recent graduates with strong analytical skills who have an interest in the insurance industry.

What are the key skills and qualifications needed to thrive as an entry level insurance data analytics professional?

To thrive as an Entry Level Insurance Data Analytics professional, you need foundational skills in statistics, data analysis, and proficiency with Excel or similar tools, often supported by a degree in mathematics, statistics, or a related field. Familiarity with data analytics software such as SQL, Python, R, and insurance industry databases is highly valuable. Strong problem-solving abilities, attention to detail, and effective communication skills set candidates apart in this role. These competencies are crucial for accurately interpreting insurance data, supporting business decisions, and conveying insights to both technical and non-technical stakeholders.

What are some common challenges faced by entry level insurance data analytics professionals, and how can they be addressed?

Entry-level professionals in insurance data analytics often encounter challenges such as working with large, complex datasets, understanding industry-specific terminology, and aligning analytical findings with business objectives. To overcome these, it's important to develop strong data management and visualization skills, seek mentorship from experienced colleagues, and regularly communicate with underwriters, actuaries, and business teams to understand the context behind the numbers. Proactively participating in team meetings and taking advantage of on-the-job training can also help bridge knowledge gaps and foster professional growth.

What is the difference between Entry Level Insurance Data Analytics vs Insurance Data Analyst?

AspectEntry Level Insurance Data AnalyticsInsurance Data Analyst
Required CredentialsBachelor's degree in data science, statistics, or related field; basic knowledge of analytics toolsBachelor's or higher in data analysis, statistics, or related; some roles prefer certifications
Work EnvironmentEntry-level roles in insurance companies, focusing on data collection and basic analysisMore experienced roles involving complex data modeling and reporting
Employer & Industry UsageInsurance companies, brokers, and agenciesInsurance firms, consulting agencies, and risk management companies

Entry Level Insurance Data Analytics positions focus on foundational data tasks within insurance firms, often requiring less experience and offering training opportunities. Insurance Data Analysts typically have more experience, handling advanced analysis and reporting. Both roles are essential in the insurance industry but differ mainly in complexity and responsibility.

What does an entry level insurance data analyst do in insurance?

An entry level insurance data analyst collects, organizes, and analyzes insurance data to identify trends, assess risks, and support decision-making. They often use tools like Excel, SQL, or data visualization software and work closely with underwriters and actuaries to improve underwriting processes and pricing strategies.

What are the most commonly searched types of Insurance Data Analytics jobs in Virginia?

The most popular types of Insurance Data Analytics jobs in Virginia are:

What are popular job titles related to Entry Level Insurance Data Analytics jobs in Virginia?

For Entry Level Insurance Data Analytics jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Entry Level Insurance Data Analytics jobs in Virginia look for?

The top searched job categories for Entry Level Insurance Data Analytics jobs in Virginia are:

What cities in Virginia are hiring for Entry Level Insurance Data Analytics jobs?

Cities in Virginia with the most Entry Level Insurance Data Analytics job openings:

Entry Level Insurance Data Analytics jobs near you
Infographic showing various Entry Level Insurance Data Analytics job openings in Virginia as of August 2026, with employment types broken down into 1% As Needed, 68% Full Time, 27% Part Time, and 4% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution.

Data Scientist (Fraud Analytics & Investigative Support)

Praescient Analytics

Fairfax, VA • On-site, Remote

Full-time

Retirement, PTO

Re-posted 25 days ago

Job description

Location: Remote (Occasional Travel May Be Required)
Clearance: Ability to obtain and maintain a Public Trust
Position Overview
Praescient Analytics is seeking multiple Data Scientists to support advanced fraud analytics and investigative initiatives for a federal oversight organization. These positions will develop, test, validate, and deploy innovative analytical solutions that help identify fraud, waste, abuse, and mismanagement across large-scale federal benefit programs and other government-funded initiatives.
Working as part of a multidisciplinary analytics team, Data Scientists will leverage statistical analysis, machine learning, data visualization, entity resolution, and risk modeling techniques to transform large, diverse datasets into actionable intelligence supporting investigators, auditors, and government decision-makers.
The ideal candidate is a technically strong, mission-focused data scientist who enjoys solving complex analytical problems while collaborating with investigators, business analysts, data engineers, forensic accountants, and government stakeholders.
Key Responsibilities
  • Develop, test, validate, and maintain fraud detection and program integrity analytics.
  • Design analytical rules and methodologies to identify fraud indicators, anomalies, suspicious activity, and emerging risks.
  • Perform exploratory data analysis, feature engineering, model development, validation, and performance evaluation.
  • Analyze structured and unstructured data from multiple public, non-public, commercial, financial, and government data sources.
  • Collaborate with Data Engineers to prepare and optimize data for analytics.
  • Support entity resolution, anomaly detection, predictive analytics, and risk scoring initiatives.
  • Produce dashboards, reports, visualizations, and analytical products that support investigative decision-making.
  • Document analytical methodologies, assumptions, validation results, and technical findings.
  • Participate in Agile delivery activities including sprint planning, demonstrations, peer reviews, and iterative model development.

Required Qualifications
  • Must have experience with Fraud Analysis
  • Three (3) or more years of professional experience in data science, applied analytics, machine learning, statistics, fraud analytics, or a related quantitative field.
  • Strong programming experience using Python and SQL.
  • Experience developing and validating analytical models.
  • Experience analyzing structured and unstructured datasets.
  • Experience documenting analytical methodologies and technical findings.
  • Strong analytical reasoning and problem-solving skills.
  • Excellent written and verbal communication skills.

Preferred Qualifications
Preference will be given to candidates with experience in one or more of the following:
  • Fraud detection, fraud prevention, financial crime analytics, or program integrity.
  • Federal benefit programs, grants, loans, healthcare, unemployment insurance, emergency assistance, disaster relief, or other public-sector programs.
  • Risk modeling, anomaly detection, entity resolution, predictive analytics, and statistical modeling.
  • Cloud analytics environments such as Azure Databricks, Microsoft SQL Server, Microsoft Fabric, Azure Data Lake Storage (ADLS), Power BI, Git repositories, or Lakehouse architectures.
  • Working with public, non-public, commercial, financial, or cross-agency datasets.
  • Data visualization and dashboard development.
  • Agile software development and analytics teams.
  • Enterprise data governance, metadata management, and data quality best practices.

What We're Looking For
We're looking for curious, collaborative data scientists who enjoy using data to solve challenging fraud detection problems. The ideal candidate combines strong analytical skills with a passion for supporting government oversight and investigative missions through innovative, data-driven solutions.
What you can expect from us:
  • Real opportunity for career growth in an environment where your achievements will be celebrated
  • Constant collaboration with numerous teams to ensure client success
  • A team that respects and embraces your ideas and expertise
  • Coworkers that are motivated by pursuing excellence, rather than the prospect of personal gain
  • A workplace dedicated to supporting and bettering public safety and government agencies

Benefits:
  • Competitive salary based on qualifications and experience
  • Comprehensive, Company paid healthcare for you (We pay your premiums and deductibles)
  • 401(k) with company match
  • Travel & performance incentives
  • 3 weeks paid time off (plus Federal Holidays)
  • $5K annual training allowance
  • $500 book allowance
  • Tuition reimbursement program

Praescient Analytics is an Equal Employment Opportunity employer. Employment decisions are based on merit, qualifications, experience, performance, business needs, and applicable contract requirements. Praescient does not unlawfully discriminate or provide disparate treatment based on race, ethnicity, color, religion, sex, national origin, age, disability, veteran status, genetic information, or any other status protected by applicable law.
Praescient Analytics acknowledges the applicable clause and provision updates implementing Executive Order 14398, Addressing DEI Discrimination by Federal Contractors, and the related FAR/RFO updates, including FAR 52.222-90 where applicable. Praescient does not engage in racially discriminatory DEI activities, including disparate treatment based on race or ethnicity in recruitment, hiring, promotion, contracting, program participation, training, mentoring, leadership development, or allocation of company resources. Praescient's employment and contracting decisions are made based on merit, qualifications, experience, performance, business needs, and applicable contract requirements.
Applicants selected will be subject to a government security investigation and must meet eligibility requirements for access to classified information.
US Citizenship Required
Interested Candidates: Please forward your resume to recruiting@praescientanalytics.com and please visit our website to apply online at www.praescientanalytics.applicantstack.com/x/openings.

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