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Entry Level Insurance Data Analytics Jobs in Virginia

... insurance data model * Participating in meetings with business stakeholders to understand analytical or operational data needs so the best data modeling approach is selected. * Designing and ...

Scientific Data Analyst

Vienna, VA · On-site

$85K - $115K/yr

Scientific Data Analyst Work Location: Remote Clearance Required: Public Trust - Federal Client ... Life Insurance, STD/LTD term disability coverage, with employer paid premiums * 401 (k) plan with a ...

Scientific Data Analyst

Vienna, VA · Remote

$85K - $115K/yr

Scientific Data Analyst Work Location: Remote Clearance Required: Public Trust - Federal Client ... Life Insurance, STD/LTD term disability coverage, with employer paid premiums * 401 (k) plan with a ...

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Senior Associate ... PwC does not intend to hire experienced or entry level job seekers who will need, now or in the ...

SimIS seeks a Data Collector/Analyst responsible for collecting, validating, and analyzing test ... Life Insurance * Flex Spending Accounts * 401(k) Savings Plan * Tuition Assistance Program

SimIS seeks a Data Collector/Analyst responsible for collecting, validating, and analyzing test ... Life Insurance * Flex Spending Accounts * 401(k) Savings Plan * Tuition Assistance Program

SimIS seeks a Data Collector/Analyst responsible for collecting, validating, and analyzing test ... Life Insurance * Flex Spending Accounts * 401(k) Savings Plan * Tuition Assistance Program

Perform data analysis to support internal and external project needs. Design basic programs for ... Health Insurance. Complementary Accommodation. contact: Mr Robert : 6822313010

... vision insurance, 401K with company matching, flexible spending accounts, paid holidays, three ... Produce cost-per-user and cost-per-wing analytics supporting ACC budget planning * Forecast future ...

... vision insurance, 401K with company matching, flexible spending accounts, paid holidays, three ... Produce cost-per-user and cost-per-wing analytics supporting ACC budget planning * Forecast future ...

... vision insurance, 401K with company matching, flexible spending accounts, paid holidays, three ... Produce cost-per-user and cost-per-wing analytics supporting ACC budget planning * Forecast future ...

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Entry Level Insurance Data Analytics information

What are some common challenges faced by entry-level professionals in insurance data analytics, 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 are the key skills and qualifications needed to thrive as an Entry Level Insurance Data Analytics professional, and why are they important?

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 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 are entry level insurance data analytics jobs?

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 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:
Infographic showing various Entry Level Insurance Data Analytics job openings in Virginia as of July 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 100% In-person job distribution.
Mid-level Data Scientist (OBI Advanced Analytic Method Augmentat with Security Clearance

Mid-level Data Scientist (OBI Advanced Analytic Method Augmentat with Security Clearance

CELESTAR CORPORATION

Reston, VA • On-site

Other

Medical, Dental, Life, Retirement, PTO

Re-posted 26 days ago


Job description

Celestar Corporation is seeking a Mid-level Data Scientist (OBI Advanced Analytic Method Augmentation) to support The Defense Intelligence Agency ( DIA) under the Object Based Intelligence and Quality Assurance (OBIQUA) task order. The primary place of performance will be at DIA Facilities across the National Capital Region (NCR). If interested and meet the qualifications, we encourage you to apply for this rewarding and impactful opportunity. ANTICIPATED AWARD: TBD ANTICIPATED START: TBD PERIOD OF PERFORMANCE: 1 Base Year + 4 Option Years LOCATION: DIA Facilities across the National Capital Region (NCR) CLEARANCE REQUIREMENT: Active TS/SCI About Us: Celestar, a proud Veteran-Owned company, offers highly competitive salaries and benefits. Our comprehensive benefits package includes company-paid employee and family dental insurance, employee health insurance, life insurance, and disability coverage. Additionally, we provide a 401(k)-retirement plan with company matching, paid holidays, and personal time off. Responsibilities: This opportunity will support multiple DIA initiatives, including the Machine-Assisted Rapid-Repository System (MARS), Object Management Services (OMS), and Object-Based Intelligence (OBI). • The Data Scientist (OBI Advanced Analytic Method Augmentation), Conducts data analytics, data engineering, data mining, exploratory analysis, predictive analysis, and statistical analysis, and uses scientific techniques to correlate data into graphical, written, visual and verbal narrative products, enabling more informed analytic decisions. • Proactively retrieves information from various sources, analyzes it for better understanding about the data set, and builds AI tools that automate certain processes. • Duties typically include: creating various ML-based tools or processes, such as recommendation engines or automated lead scoring systems. • Performs statistical analysis, applies data mining techniques, and builds high quality prediction systems. • Should be skilled in data visualization and use of graphical applications, including Microsoft Office (Power BI) and Tableau; major data science languages, such as Rand Python; managing and merging of disparate data sources, preferably through R, Python, or SQL; statistical analysis; and data mining algorithms. • Should have prior experience with large data multi-INT analytics, ML, and automated predictive analytics. • Designs, develops, and evaluates leading-edge algorithmic intelligence concepts, practices, and technologies for implementation into all-source analysis tradecraft, assessments, production, and dissemination. • Proposes advanced statistical or mathematical techniques and methodology that may permit identification and evaluation of alternatives, assists in model formulation or experimental test design, and shares jointly in team responsibility for development of advanced analytic techniques and assessments. • Evaluates data science, artificial intelligence, and other advanced analytic methods for risks, biases, and limitations that would distort conclusions. • Conducts continuous independent research on methods of analysis in government, industry, and academia to keep abreast of the state of the art, keeps senior leadership apprised of the advances and applicability to programs. • Utilizes in-depth knowledge of relevant theories, techniques, procedures and processes to investigate, prototype, and evaluate technologies to improve all-source intelligence analysis. • Provides technical input into and participates in the development of software and computer graphics systems. • Performs research studies to understand the process of augmenting or automating all source analytic processes using various computer models. • Provides incremental enhancements to tools, capabilities, processes, and methods. • Possesses in-depth knowledge and experience in using data analytics, data engineering, data mining, exploratory analysis, predictive analysis, and statistical analysis, and scientific techniques to correlate data into graphical, written, visual and verbal narrative products, enabling more informed analytic decisions. • Writes either R or Python scripts to drive data science workflows, have experience using SQL, and managing and merging of disparate data sources, preferably through R, Python, or SQL; statistical analysis; and data mining algorithms. • Possesses prior experience with large data, spatial data, multi-INT analytics, ML, and automated predictive analytics. • Works with ambiguous information, deconstruct key questions, leverage spatial data, exploit application programming interfaces, suggest methodologies, develop data schemas to structure observations. This requires working knowledge of coding and scripting, information science, mathematics, machine learning, visual analytic modeling tools, and relevant Standard Operating Procedures (SOPs) to create repeatable, widely applicable procedures to support all-source intelligence analysis and production. • Creates and works in distributed analytic environments, scaling algorithms to work on increasingly large and complex datasets that are larger than RAM. • Serves as the primary POC for data science expertise, ensuring tradecraft compliance and analytic standards as it relates to data science techniques on the contract. • Provides advice on emerging data science methods, tools, algorithms, training, or requirements to advance DIA's analytic edge in its use of data science. • Works with DIA vendors and the software developers to implement distributed algorithms to work on increasingly large and complex data sets. • Review and evaluate OBI documentation submitted by advanced analytic (AA) owners to ensure compliance with tradecraft standards and adherence to best practices in AI system development and deployment. • Assess OBI documentation for completeness, accuracy, and thoroughness, and provide detailed feedback to owners and developers. • Provide consultation and guidance to data and AA owners, developers, and stakeholders on OBI governance and knowledge modeling, including best practices for system development, testing, and deployment. • Assist analytic methodologists and AA owners in translating technical documentation into analytic tradecraft compliant language. • Collaborate with stakeholders to develop, implement, and refine best practices for translating technical documentation into tradecraft compliant language • Review and edit translated documentation to ensure accuracy, completeness, and adherence to tradecraft standards. • Collaborate with the Computer Scientist to develop and implement testing methodologies for system validation and evaluation. • Conduct audits to ensure compliant use of systems for approved use-cases in all source analysis. • Develop and maintain a repository of audit findings and recommendations to facilitate knowledge sharing and best practices across the organization. • Design and execute TEVV protocols to evaluate the performance, robustness, and fairness of systems in all source analysis contexts. • Develop and apply statistical models and methods to analyze TEVV results and identify areas for improvement. • Collaborate with stakeholders to develop and implement corrective actions to address TEVV findings. • Develop and track performance metrics to evaluate the effectiveness of systems in all source analysis. • Analyze and interpret performance metrics to identify trends, patterns, and areas for improvement. • Collaborate with stakeholders to develop and implement data-driven decision-making processes to inform system development and improvement. • Develop and refine methodologies for evaluating system performance, robustness, and fairness in all source analysis contexts. • Collaborate with stakeholders to develop and implement best practices for system development, testing, and deployment. • Supports capability development by contributing, editing, and storing code in Government owned/controlled source version control repositories. Required qualifications/skills: • Minimum 8 years of experience related to the specific labor category with at least a portion of the experience within the last 2 years with a bachelor's degree. Come on board with a company that Values its Employees! Celestar Corporation is an Equal Opportunity Employer. The Celestar Corporation prohibits discrimination, harassment, and retaliation in employment based on race; color; religion; genetic information; national origin; sex (including same-sex); sexual orientation; gender identity; pregnancy, childbirth, or related medical conditions; age; disability or handicap; citizenship status; marital status; service member/protected veteran status; or any other category protected by federal, state, or local law.