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Hybrid Science Jobs in Virginia (NOW HIRING)

Altagrove LLC

Data Science

Norfolk, VA · On-site

Support cloud-native and hybrid data environments leveraging AWS, Azure, Kubernetes, and modern ... Strong proficiency in Python and experience with modern data science and engineering frameworks.

SiloSmashers

Software Engineer

Arlington, VA · On-site

Arlington, VA (Hybrid) Clearance: Must be a U.S. citizen and able to obtain and maintain DHS ... Bachelor's degree in Computer Science, Engineering, or related field. * 5+ years of professional ...

THOMSON REUTERS

Data Scientist

Mclean, VA · Hybrid

Willingness to work full time in a hybrid work environment in theWashingtonDC area. * Bachelor's or Master'sDegree in a technical field (e.g., data science, statistics, computer science, mathematics ...

Booz Allen Hamilton

Lead Data Science Engineer

Mclean, VA · On-site

$99K - $225K/yr

As a Data Science Engineer, you'll help build advanced technology solutions and implement data ... Hybrid : If this position is listed as hybrid, you will be expected to work from a Booz Allen ...

Elder Research

Data Scientist

Arlington, VA · Hybrid

This is a hybrid position with several days onsite per month as needed. You'll support a U.S ... Help strengthen the client's internal data-science capability through pairing, code review ...

Elder Research

Data Scientist

Arlington, VA · Hybrid

This is a hybrid position with several days onsite per month as needed. You'll support a U.S ... Help strengthen the client's internal data-science capability through pairing, code review ...

Elder Research

Data Scientist

Arlington, VA · On-site

This is a hybrid position with several days onsite per month as needed. You'll support a U.S ... Help strengthen the client's internal data-science capability through pairing, code review ...

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Hybrid Science information

What is the difference between Hybrid Science vs Data Scientist?

AspectHybrid ScienceData Scientist
Required CredentialsScience degrees, certifications in data analysis, programmingStatistics, computer science, data analysis certifications
Work EnvironmentResearch labs, industry settings, interdisciplinary teamsTech companies, research firms, consulting
Employer & Industry UsageResearch institutions, biotech, environmental agenciesTech, finance, healthcare, marketing

Hybrid Science professionals combine scientific expertise with data analysis skills to interpret complex data in research and industry. Data Scientists focus primarily on analyzing large datasets to inform business decisions. While both roles require strong analytical skills and technical knowledge, Hybrid Science emphasizes interdisciplinary scientific understanding alongside data analysis, making it ideal for research-driven environments.

What is a Hybrid Scientist?

A Hybrid Scientist is a professional who combines expertise from multiple scientific disciplines, often integrating skills from fields such as biology, computer science, engineering, or data analytics. They use interdisciplinary approaches to solve complex problems that cannot be addressed by a single traditional field. Hybrid Scientists are commonly found in areas like biotechnology, environmental science, and artificial intelligence, where the blending of knowledge leads to innovative solutions. Their unique skill set makes them valuable in research, development, and product innovation roles.

How does a Hybrid Science professional typically collaborate with interdisciplinary teams, and what challenges might arise in such a setting?

Hybrid Science professionals often work at the intersection of multiple scientific disciplines, such as biology, chemistry, and data science. Collaboration involves frequent communication with specialists from different backgrounds to integrate diverse methodologies and perspectives. One common challenge is bridging terminology gaps and aligning goals across disciplines, which requires strong interpersonal and project management skills. Successfully navigating these collaborations not only advances projects but also broadens your expertise and opens up opportunities for leadership roles.

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

To thrive as a Hybrid Scientist, you need a strong interdisciplinary background combining life sciences, computational skills, and analytical problem-solving, often supported by advanced degrees in fields like bioinformatics, computational biology, or data science. Familiarity with programming languages (e.g., Python, R), data analysis tools, laboratory techniques, and relevant certifications such as GLP or GCP are typically required. Excellent communication, adaptability, and collaboration skills distinguish top performers in this role. These abilities are crucial for integrating diverse scientific domains, driving innovation, and effectively contributing to complex, cross-functional research projects.
What cities in Virginia are hiring for Hybrid Science jobs? Cities in Virginia with the most Hybrid Science job openings:
Hybrid Science jobs near you
Infographic showing various Hybrid Science job openings in Virginia as of July 2026, with employment types broken down into 88% Full Time, 8% Part Time, 2% Temporary, and 2% Contract. Highlights an 62% Physical, 28% Hybrid, and 10% Remote job distribution.

Data Science

Altagrove LLC

Norfolk, VA • On-site

Full-time

Re-posted 28 days ago

Job description

Salary:

Who we are:


Altagrove delivers smart and innovative technology solutions that create competitive advantages for our customers and their missions. Our focus areas include Space, Connectivity, Cyber, Cloud, Analytics, and Research & Development. As we continue to grow, Altagrove is actively recruiting for a Data Scientist to join our energetic and entrepreneurial team that is executing on a variety of projects that are technology oriented. A successful candidate will bring a core area of expertise and a passion for learning and implementing new ideas in a start-up environment.


Follow us at -https://www.linkedin.com/company/altagrove


What you will do:


  • Design, develop, deploy, and maintain AI-enabled analytics solutions supporting operational and strategic mission objectives.
  • Build and optimize enterprise data pipelines, ingestion frameworks, transformation workflows, and integration services supporting analytics and AI platforms.
  • Develop machine learning models, predictive analytics capabilities, and decision-support solutions using structured and unstructured data.
  • Design and implement Large Language Model (LLM) solutions, Retrieval-Augmented Generation (RAG) architectures, vector databases, and AI-enabled knowledge management capabilities.
  • Develop scalable data architectures, metadata enrichment pipelines, indexing services, and retrieval systems supporting enterprise knowledge exploitation.
  • Collaborate with mission stakeholders, engineers, and technical teams to identify high-value AI and data-driven use cases.
  • Conduct data exploration, feature engineering, model training, validation, testing, and performance optimization activities.
  • Design and implement ETL/ELT processes supporting operational, analytical, and machine learning workloads.
  • Develop dashboards, visualizations, reports, and analytical products that communicate insights to technical and non-technical stakeholders.
  • Support cloud-native and hybrid data environments leveraging AWS, Azure, Kubernetes, and modern data engineering technologies.
  • Implement data quality controls, monitoring solutions, security controls, and governance practices across enterprise data environments.
  • Work closely with Cloud Engineers, DevSecOps Engineers, and Software Developers to support end-to-end solution delivery.
  • Research emerging AI, machine learning, and data engineering technologies and recommend innovative applications for customer missions.
  • Support technical documentation, architecture development, operational procedures, training materials, and knowledge transfer activities.


What you will bring:


  • Experience developing and deploying advanced analytics, machine learning, artificial intelligence, or enterprise data engineering solutions within government, defense, intelligence, or highly regulated environments.
  • Strong proficiency in Python and experience with modern data science and engineering frameworks.
  • Experience with machine learning frameworks such as Scikit-Learn, TensorFlow, PyTorch, or similar technologies.
  • Experience designing and maintaining enterprise data pipelines, ETL/ELT workflows, and data integration architectures.
  • Familiarity with Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), vector databases, embeddings, and prompt engineering concepts.
  • Experience working with SQL, NoSQL, data lakes, data warehouses, and cloud-native data platforms.
  • Experience with AWS, Azure, Google Cloud, or hybrid cloud environments.
  • Understanding of data governance, metadata management, data quality, security, and compliance principles.
  • Experience with containerization technologies, Kubernetes, DevSecOps practices, and Infrastructure-as-Code is highly desirable.
  • Strong analytical, problem-solving, and communication skills.
  • Ability to collaborate effectively across multidisciplinary engineering and mission teams.
  • Experience supporting NATO, DoD, Intelligence Community, or other national security organizations is highly desired.
  • Bachelors or Masters degree in Data Science, Computer Science, Data Engineering, Mathematics, Statistics, Engineering, Information Systems, or a related discipline.
  • Active Secret Clearance required; TS/SCI or NATO Secret preferred.
  • Exceptional attention to detail and organizational skills.