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Data Science Software Engineer Jobs in Virginia (NOW HIRING)

Bachelor's degree in computer science, Software Engineering, or related field. Required: * TS/SCI with Full Scope Polygraph * BS in a quantitative field (mathematics, data science, statistics) * At ...

S. Citizenship • Bachelor's Degree in Computer Science, Software Engineering, Information Technology, Computer Engineering, Electrical Engineering, Data Science, Cybersecurity, Mathematics ...

S. Citizenship • Bachelor's Degree in Computer Science, Software Engineering, Information Technology, Computer Engineering, Electrical Engineering, Data Science, Cybersecurity, Mathematics ...

S. Citizenship • Bachelor's Degree in Computer Science, Software Engineering, Information Technology, Computer Engineering, Electrical Engineering, Data Science, Cybersecurity, Mathematics ...

... Data Science, Mechanical Engineering, Aerospace Engineering, Computational Biology, Computational Chemistry, Information Technology, Systems Engineering, Artificial Intelligence, High-Performance ...

Data Engineering Lead

Arlington, VA · On-site

$131K - $158K/yr

Bachelor's degree (or 4 years of additional equivalent experience) in IT, Cybersecurity, Computer Science, Information Systems, Data Science, Software Engineering Security Clearance: * Active Secret ...

Showing results 21-40

Data Science Software Engineer information

See Virginia salary details

$44.1K

$128.6K

$176K

How much do data science software engineer jobs pay per year?

As of Aug 23, 2026, the average yearly pay for data science software engineer in Virginia is $128,604.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,500.00 and $136,300.00 per year, depending on experience, location, and employer.

What is a data science software engineer?

A Data Science Software Engineer is a professional who combines software engineering skills with data science expertise to build scalable data-driven systems and applications. They design, develop, and optimize software that supports data pipelines, machine learning models, and analytics platforms. Their work bridges the gap between data scientists, who focus on statistical analysis and modeling, and traditional software engineers, who focus on building robust and efficient software systems. Data Science Software Engineers ensure that data solutions are production-ready, scalable, and maintainable.

What are the key skills and qualifications needed to thrive as a data science software engineer?

To thrive as a Data Science Software Engineer, you need strong proficiency in programming (especially Python or R), a solid understanding of statistics and algorithms, and typically a degree in computer science, data science, or a related field. Familiarity with machine learning frameworks (such as TensorFlow or scikit-learn), data processing tools (like Spark or Hadoop), and cloud platforms (AWS, GCP, or Azure) is essential, as are relevant certifications. Excellent problem-solving abilities, communication skills, and the ability to work collaboratively with cross-functional teams set top performers apart. These competencies are vital for efficiently developing scalable data-driven solutions that drive business insights and innovation.

How does a data science software engineer typically collaborate with data scientists and other stakeholders on projects?

Data Science Software Engineers play a vital role in bridging the gap between data science and software engineering teams. They work closely with data scientists to translate prototypes and models into scalable, production-ready code, and often collaborate with product managers, analysts, and infrastructure engineers to ensure seamless integration. Regular communication and code reviews are essential, as is an iterative development process to address feedback and ensure solutions meet both technical and business requirements. This cross-functional collaboration helps deliver robust data-driven applications that align with organizational goals.

What is the difference between Data Science Software Engineer vs Data Analyst?

AspectData Science Software EngineerData Analyst
Required SkillsProgramming, software development, machine learningData visualization, statistical analysis, reporting
Work EnvironmentSoftware development teams, engineering projectsBusiness units, reporting teams
Common ToolsPython, Java, SQL, ML frameworksExcel, Tableau, SQL, R
Industry UsageTech, finance, healthcare, startupsMarketing, finance, retail, research

While both roles analyze data, Data Science Software Engineers focus on developing software solutions and machine learning models, requiring strong programming skills. Data Analysts primarily interpret data through visualization and statistical methods to support business decisions. The roles often overlap but serve different functions within organizations.

What are popular job titles related to Data Science Software Engineer jobs in Virginia?

For Data Science Software Engineer jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Data Science Software Engineer jobs in Virginia look for?

The top searched job categories for Data Science Software Engineer jobs in Virginia are:

Infographic showing various Data Science Software Engineer job openings in Virginia as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 19% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $128,604 per year, or $61.8 per hour.

Data Scientist / AI Engineer

Ironclad Defense Works

Norfolk, VA • On-site

$115 - $130/hr

Other

Medical, Dental, Retirement, PTO

Posted 20 days ago


Job description

Data Scientist / AI Engineer

Location: Norfolk, Virginia

Employment Type: Full-time, On-site

Security Clearance: Active NATO or U.S. National SECRET clearance required

Citizenship: Must be a citizen of a NATO member nation

The Role

Ironclad is seeking an experienced Data Scientist / AI Engineer to support the development and implementation of advanced data science, artificial intelligence, and large language model capabilities within the NATO enterprise.

This position requires a technically versatile professional who can bridge data engineering, software development, machine learning, and operational mission requirements. The selected candidate will design scalable data architectures, build and optimize data pipelines, develop API-based infrastructure, and support the secure deployment of AI and machine learning solutions in cloud-based and hybrid environments.

The role requires strong hands-on experience with generative AI, large language models (LLMs), distributed systems, microservices, containerized applications, and modern software engineering practices. The successful candidate must also be able to translate complex operational challenges into practical technical solutions for military and civilian stakeholders.

Key Responsibilities
  • Develop and implement scalable data science and AI capabilities supporting NATO initiatives.
  • Design, build, and maintain data pipelines for structured and unstructured data.
  • Prepare, cleanse, transform, and optimize data for LLM training, fine-tuning, inference, and analytics.
  • Develop API-based infrastructure that integrates LLMs and machine learning models with operational systems.
  • Design and support microservices and containerized AI/ML applications.
  • Build distributed data storage and processing solutions using cloud-based or hybrid architectures.
  • Develop real-time data processing and streaming capabilities for operational decision support.
  • Automate data engineering processes and improve the scalability, efficiency, and reliability of AI infrastructure.
  • Implement monitoring, logging, traceability, and performance-optimization tools for data pipelines and APIs.
  • Support the secure deployment of AI and LLM solutions in Microsoft Azure, AWS, or comparable environments.
  • Develop tools that improve data accessibility for data scientists, analysts, engineers, and operational users.
  • Collaborate with data scientists, software engineers, system architects, and other technical stakeholders.
  • Support federated learning, cross-domain data sharing, and secure collaboration across NATO nations.
  • Develop proofs of concept for LLM-based and advanced analytics applications.
  • Evaluate operational requirements and recommend appropriate AI, software, and data-engineering solutions.
  • Create dashboards, reports, and visual analytics for senior and non-technical stakeholders.
  • Provide technical briefings, mentoring, and training in AI engineering, data science, API development, and digital literacy.
  • Research emerging developments in generative AI, distributed computing, data architecture, and software engineering.
  • Promote responsible, secure, and ethical AI practices throughout solution development and deployment.
Required Qualifications
  • Bachelor’s degree or higher from a nationally recognized university in data science, data analytics, artificial intelligence, mathematics, physics, computer science, software engineering, or a closely related discipline.
  • At least four years of professional experience as a Data Scientist, Machine Learning Engineer, Data Engineer, Software Engineer, or in a closely related role.
  • Demonstrated experience developing operational AI or machine learning solutions.
  • Experience with distributed systems and cloud-based or hybrid architectures.
  • Experience designing API-based infrastructure and microservices architectures.
  • Hands‑on experience developing and deploying containerized applications using technologies such as Docker or Kubernetes.
  • Demonstrated experience with generative AI and large language models.
  • Experience preprocessing data and supporting the fine‑tuning and deployment of LLMs in secure, scalable environments.
  • Experience with machine learning frameworks such as TensorFlow, PyTorch, scikit‑learn, or comparable technologies.
  • Strong programming experience with Python, Java, Scala, or similar languages.
  • Experience with version control, CI/CD pipelines, automated testing, and modern software engineering practices.
  • Experience building and optimizing ETL processes, data pipelines, and real‑time streaming solutions.
  • Familiarity with Apache Airflow, Kafka, Spark, or comparable data‑engineering technologies.
  • Experience architecting or maintaining data lakes, data warehouses, distributed storage systems, or NoSQL solutions.
  • Knowledge of platforms such as Delta Lake, Snowflake, Hadoop, or comparable technologies.
  • Experience applying AI to operational decision support and the analysis of unstructured data, including text or imagery.
  • Strong understanding of data security, privacy, sovereignty, and responsible AI practices.
  • Experience developing dashboards, visual reports, and analytics using Tableau, Microsoft Power BI, Kibana, or comparable tools.
  • Ability to translate operational problems into practical AI and machine learning solutions.
  • Demonstrated success working with multidisciplinary technical teams.
  • Strong written and verbal communication skills.
  • Ability to explain technical concepts to non‑technical stakeholders and senior leaders.
  • Ability to mentor or train personnel in AI engineering, data science, or software development concepts.
Preferred Qualifications
  • Familiarity with NATO processes, organizational structures, operational culture, and decision‑making procedures.
  • Experience supporting military, defense, government, or international organizations.
  • Experience developing AI or data‑engineering solutions using open‑source frameworks and publicly available datasets.
  • Familiarity with military staff workflows and operational planning processes.
  • Experience with federated learning and privacy‑preserving collaboration across multiple organizations or nations.
  • Experience supporting cross‑domain data sharing and API‑driven interoperability.
  • Familiarity with agile project‑management methods and tools such as JIRA, Trello, or Microsoft Loop.
  • Experience briefing senior leaders and presenting actionable, data‑driven recommendations.
  • Knowledge of ethical AI principles, including bias mitigation, responsible data handling, transparency, and secure deployment.
Why Join Ironclad

Ironclad supports complex defense and international missions by providing experienced professionals who combine technical expertise with an understanding of operational requirements. This position offers the opportunity to contribute directly to secure, scalable, and mission‑focused AI capabilities while working alongside military, civilian, and technical stakeholders across the NATO enterprise.

Clearance

This position requires anactive NATO or National SECRET (or higher) security clearance. Applicants who do not possess the clearance specified above cannot be considered at this time.

Compensation

Compensation for this position ranges from $115,000 - $130,000 annually. Final salary will be based on factors such as experience, education, skills, qualifications, contract requirements, and overall affordability.

Eligible full‑time employees may also receive a comprehensive benefits package, including medical and dental insurance, retirement benefits, paid leave, and professional development opportunities.

Ironclad Defense Works is an Equal Opportunity Employer.

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