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Computational Data Science Jobs in Virginia (NOW HIRING)

Scientific Data Analyst

Arlington, VA · On-site +1

$110K - $115K/yr

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 ...

Scientific Data Analyst

Arlington, VA · On-site

$110K - $115K/yr

In this role, you will provide computational, data science, and bioinformatics support for Office of Research Innovation, Validation, and Application's (ORIVA) research validation and application ...

Data Science SME Mid

Fort Belvoir, VA · On-site

$115K - $157K/yr

... e., Computational Thinking, Programming, Statistics, or Data Visualization). * Education ... science and programming, data modeling, Artificial intelligence and machine learning. What We Value

Data Science SME Senior

Fort Belvoir, VA · On-site

$120K - $171K/yr

Data Science SME Senior TULK supports U.S. national security customers with cleared experts who ... e., Computational Thinking, Programming, Statistics, or Data Visualization). * Education:

Data Science SME Lead

Fort Belvoir, VA · On-site

$120K - $189K/yr

Data Science SME Lead TULK supports U.S. national security customers with cleared experts who ... e., Computational Thinking, Programming, Statistics, or Data Visualization). * Education:

... e., Computational Thinking, Programming, Statistics, or Data Visualization). * Minimum 5 years of experience focusing on the concepts and applications of data analytics, computer science and ...

Applied Mathematics (e.g., probability and statistics, formal modeling, computational social ... Computer Programming (e.g., programming languages, math/statistics packages, computer science ...

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Computational Data Science information

See Virginia salary details

$16

$56

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How much do computational data science jobs pay per hour?

As of Jun 27, 2026, the average hourly pay for computational data science in Virginia is $56.33, according to ZipRecruiter salary data. Most workers in this role earn between $46.25 and $66.73 per hour, depending on experience, location, and employer.

What does a computational data scientist do?

A computational data scientist analyzes large datasets using programming languages like Python or R, develops algorithms, and applies statistical models to extract insights and solve complex problems. They often work with machine learning tools and require strong analytical skills to support data-driven decision-making in organizations.

What is the difference between Computational Data Science vs Data Analyst?

AspectComputational Data ScienceData Analyst
Required CredentialsTypically requires a degree in Computer Science, Data Science, or related fields; often includes programming certificationsUsually requires a degree in Statistics, Business, or related fields; may include basic data analysis certifications
Work EnvironmentInvolves programming, modeling, and developing algorithms; often in tech or research settingsFocuses on interpreting data, creating reports, and supporting decision-making; in business or corporate environments
Employer & Industry UsageUsed in tech companies, research institutions, and industries requiring advanced modelingCommon in finance, marketing, healthcare, and business sectors

Computational Data Science involves advanced programming, algorithm development, and modeling, often in technical environments. Data Analysts focus on interpreting data, generating reports, and supporting business decisions. While both roles work with data, Computational Data Scientists typically require stronger programming skills and work on building models, whereas Data Analysts focus on data interpretation and visualization.

What is Computational Data Science?

Computational Data Science is an interdisciplinary field that combines computer science, statistics, and domain knowledge to extract insights and knowledge from complex data sets using computational techniques. Professionals in this field use algorithms, machine learning, and advanced analytics to solve real-world problems by processing and interpreting large volumes of data. The work often involves programming, data modeling, and visualization, making it crucial in industries such as healthcare, finance, and technology. Computational Data Scientists help organizations make data-driven decisions and innovate through predictive modeling and data analysis.

Is 40 too late for data science?

Computational Data Science is a field where individuals can enter at any age, as success depends on skills, experience, and continuous learning. Many professionals transition into data science later in their careers by acquiring relevant knowledge in programming, statistics, and machine learning through online courses or certifications.

What are some common challenges faced by computational data scientists when working on cross-functional teams?

Computational data scientists often collaborate closely with professionals from diverse backgrounds, such as software engineers, domain experts, and business stakeholders. One common challenge is translating complex technical findings into actionable insights for non-technical team members. Additionally, aligning project goals and expectations across disciplines can require extra communication and flexibility. Overcoming these challenges often involves developing strong interpersonal skills, proactively clarifying requirements, and fostering a collaborative team culture.

Which is better, DS or CS?

Computational Data Science and Computer Science are related fields, but Data Science focuses on analyzing and interpreting data using statistical and machine learning techniques, while Computer Science emphasizes algorithms, programming, and software development. The choice depends on your career goals; Data Science roles often require skills in statistics, data visualization, and tools like Python or R, whereas Computer Science roles may focus more on software engineering and systems design.

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

To thrive as a Computational Data Scientist, you need a strong background in mathematics, statistics, programming (especially Python or R), and data analysis, often supported by a relevant degree in computer science, statistics, or a related field. Proficiency with data manipulation tools (like Pandas, NumPy), machine learning frameworks (such as TensorFlow or Scikit-learn), and cloud computing platforms is highly valued, along with experience using data visualization tools. Critical thinking, problem-solving, communication, and collaboration skills make someone stand out in this role. These abilities are crucial for extracting actionable insights from complex data, building effective models, and communicating findings to drive informed business decisions.

Is computational science a good career?

Computational Data Science is a growing field that combines programming, statistical analysis, and domain knowledge to solve complex problems using data. It offers opportunities in industries such as technology, finance, healthcare, and research, often requiring skills in machine learning, data visualization, and programming languages like Python or R. The career typically involves continuous learning and can provide competitive salaries and job stability.
What are popular job titles related to Computational Data Science jobs in Virginia? For Computational Data Science jobs in Virginia, the most frequently searched job titles are:
What cities in Virginia are hiring for Computational Data Science jobs? Cities in Virginia with the most Computational Data Science job openings:
Infographic showing various Computational Data Science job openings in Virginia as of June 2026, with employment types broken down into 90% Full Time, 7% Part Time, and 3% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $117,157 per year, or $56.3 per hour.

Scientific Data Analyst

IMAGINEEER LLC

Arlington, VA • On-site, Remote

$110K - $115K/yr

Full-time

Medical, Retirement, PTO

Posted 17 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.