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Computational Data Analytics Jobs in Virginia (NOW HIRING)

IMAGINEEER LLC

Scientific Data Analyst

Arlington, VA · On-site

$110K - $115K/yr

In this role, you will provide computational, data science, and bioinformatics support for Office ... You will develop and maintain NIH databases, build analytical tools, support computational modeling ...

Imagineeer LLC

Scientific Data Analyst

Arlington, VA · On-site

$110K - $115K/yr

In this role, you will provide computational, data science, and bioinformatics support for Office ... Develop and execute analytical workflows to process, analyze, and interpret large-scale biomedical ...

Level Up Llc

Data Scientist

Reston, VA · On-site

Computational Social Science * Computer Science * Data Analytics * Economics * Engineering * Geospatial Analysis * Mathematics * Operations Research * Quantitative Finance * Statistics * Experience ...

AVIAN, LLC

Data Scientist

Reston, VA · On-site +1

Computational Social Science * Computer Science * Data Analytics * Economics * Engineering * Geospatial Analysis * Mathematics * Operations Research * Quantitative Finance * Statistics * Experience ...

Level Up Llc

Data Scientist

Reston, VA · On-site +1

Computational Social Science * Computer Science * Data Analytics * Economics * Engineering * Geospatial Analysis * Mathematics * Operations Research * Quantitative Finance * Statistics * Experience ...

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All JobsComputational Data Analytics JobsComputational Data Analytics Jobs in Virginia

Computational Data Analytics information

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$93

How much do computational data analytics jobs pay per hour?

As of Jul 14, 2026, the average hourly pay for computational data analytics in Virginia is $54.28, according to ZipRecruiter salary data. Most workers in this role earn between $43.61 and $61.49 per hour, depending on experience, location, and employer.

What is computational data analysis?

Computational data analysis is a process used in data analytics roles to examine large datasets using algorithms, statistical models, and programming tools like Python or R. It involves cleaning, processing, and interpreting data to extract meaningful insights and support decision-making.

Which is better, DS or CS?

For a Computational Data Analytics role, both Data Science (DS) and Computer Science (CS) provide valuable skills; DS focuses on data analysis, modeling, and visualization, while CS emphasizes algorithms, programming, and software development. The choice depends on the specific job requirements and your career goals, but proficiency in programming languages like Python or R and understanding of data management are essential in both fields.

How does a Computational Data Analyst typically collaborate with cross-functional teams to deliver data-driven insights?

Computational Data Analysts frequently work alongside professionals from various departments, such as engineering, product management, and business strategy. They gather requirements, clarify analysis goals, and present findings in clear, actionable terms. Regular meetings and collaborative tools are often used to ensure alignment, while analysts translate complex data patterns into practical recommendations that support decision-making across the organization. This teamwork not only enhances the impact of their analyses but also provides valuable opportunities for learning and professional growth.

What are the key skills and qualifications needed to thrive as a Computational Data Analytics professional, and why are they important?

To thrive as a Computational Data Analytics professional, you need strong quantitative skills, proficiency in statistics, and expertise in data manipulation, typically supported by a degree in computer science, mathematics, or a related field. Familiarity with programming languages like Python or R, experience with data visualization tools (e.g., Tableau, Power BI), and knowledge of machine learning frameworks are commonly required. Excellent problem-solving abilities, effective communication, and the capacity to work collaboratively make candidates stand out. These skills enable professionals to extract actionable insights from complex datasets, drive informed decision-making, and add significant value to organizations.

Is 40 too late for data science?

Computational Data Analytics professionals can enter the field at any age, as success depends on skills, experience, and continuous learning. Many data scientists start or transition into the field later in life by acquiring relevant certifications, programming skills, and domain knowledge, making age less of a barrier than skill development and adaptability.

What is the difference between Computational Data Analytics vs Data Scientist?

AspectComputational Data AnalyticsData Scientist
Required CredentialsBachelor's or Master's in Data Science, Computer Science, or related fieldsBachelor's or Master's in Data Science, Computer Science, Statistics, or related fields
Work EnvironmentData analysis teams, research labs, tech companiesData analysis teams, research labs, tech companies
Employer & Industry UsageTech, finance, healthcare, academiaTech, finance, healthcare, academia
Common Search & ComparisonYesYes

Computational Data Analytics focuses on developing algorithms and computational methods to analyze large datasets, often emphasizing programming and algorithm design. Data Scientists combine statistical analysis, machine learning, and domain expertise to interpret data and generate insights. While both roles require similar educational backgrounds and work environments, Computational Data Analytics leans more toward algorithm development, whereas Data Scientists focus on modeling and interpretation.

What is computational data analytics?

Computational data analytics is the process of using computational methods, algorithms, and systems to analyze large and complex datasets. This field combines principles from computer science, mathematics, and statistics to extract meaningful insights and patterns from data. Professionals in computational data analytics use tools such as machine learning, data mining, and statistical modeling to solve real-world problems in various industries. Their work often involves programming, data visualization, and working with big data platforms.

What is the highest paying job in data analytics?

In data analytics, senior roles such as Data Science Director, Chief Data Officer, or Analytics Executive typically have the highest salaries, often exceeding six figures annually. These positions require advanced skills in machine learning, statistical analysis, and leadership, along with extensive experience and often advanced degrees or certifications.
What are popular job titles related to Computational Data Analytics jobs in Virginia? For Computational Data Analytics jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Computational Data Analytics jobs in Virginia look for? The top searched job categories for Computational Data Analytics jobs in Virginia are:
Computational Data Analytics jobs near you
Infographic showing various Computational Data Analytics job openings in Virginia as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $112,896 per year, or $54.3 per hour.

Scientific Data Analyst

IMAGINEEER LLC

Arlington, VA • On-site

$110K - $115K/yr

Full-time

Medical, Retirement, PTO

Posted 4 days ago

Job description

Benefits:
  • 401(k) matching
  • Competitive salary
  • Health insurance
  • Paid time off

About this Role:
In this role, you will provide computational, data science, and bioinformatics support for Office of Research Innovation, Validation, and Applications (ORIVA) research validation and application programs across D-NICEATM and DAIBR.
You will develop and maintain NIH databases, build analytical tools, support computational modeling, and deliver data-driven insights to a multidisciplinary team advancing human-centered biomedical research. This role sits at the intersection of data science, regulatory science, and biomedical research.
Key Responsibilities:
  1. Develop and execute analytical workflows to process, analyze, and interpret large-scale biomedical datasets
  2. Apply computational and bioinformatics methods in support of New Approach Methodologies (NAMs) related research objectives
  3. Maintain and enhance NIH databases and tools through data curation, integration, and interoperability support
  4. Build and maintain data pipelines and analytical tools using Python, R, or equivalent languages
  5. Develop and update user support materials, websites, and training resources
  6. Ensure all published materials meet 508-compliance standards
  7. Present complex analytical results clearly to scientific and non-technical audiences
  8. Support data validation, cleansing, and governance per NIH data quality standards

Qualifications and Skills:
  1. PhD in bioinformatics, mathematics, statistics, computer science, data science, or related field with 4+ years of relevant experience OR Master's degree in an equivalent field with 4 years of additional experience
  2. Proficiency in at least two programming languages: Python, R, Perl, or equivalent
  3. Experience with bioinformatics tools, databases, and high-throughput data analysis
  4. Experience managing and integrating large biomedical or genomic datasets
  5. Strong background in statistical analysis, data modeling, and computational methods
  6. U.S. citizenship required; ability to obtain Public Trust clearance

Desired Skills and Competencies:
  1. Expertise in NAMs or computational toxicology
  2. Experience with NIH scientific databases and platforms
  3. Familiarity with in silico modeling, Adverse Outcome Pathway (AOP) frameworks, or ICCVAM/OECD validation standards
  4. Prior NIH and/or HHS contractor experience
  5. Experience with cloud platforms or HPC environments
  6. Peer-reviewed publications in a related field

Equal Opportunity Employer:
We are an Equal Opportunity Employer and do not discriminate in employment decisions on the basis of race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other status protected by applicable federal, state, or local laws. All employment decisions are based on business needs, job requirements, and individual qualifications.

Flexible work from home options available.