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Remote Building Science Jobs in Virginia (NOW HIRING)

GRVTY

Data Scientist, Senior

Chantilly, VA · On-site +1

Bachelor's degree in Data Science, Computer Science, Mathematics, Statistics, or a closely related ... Experience building and deploying end-to-end analytical pipelines -- not just exploratory analysis ...

Booz Allen Hamilton, Inc.

Data Scientist

Ashburn, VA · On-site +1

$77K - $176K/yr

Remote Work: Hybrid Job Number: R0245759 Location: Ashburn,VA,US Share job via: Share Data ... On our team, you'll use your leadership skills and data science expertise to create real-world ...

Booz Allen Hamilton, Inc.

Data Scientist, Mid

Arlington, VA · On-site +1

$77K - $176K/yr

Remote Work: No Job Number: R0241792 Location: Arlington,VA,US Share job via: Share Data Scientist ... Experience building and applying machine learning techniques * Ability to travel up to 25% of the ...

Booz Allen Hamilton, Inc.

Data Scientist, Mid

Arlington, VA · On-site +1

$77K - $176K/yr

Remote Work: No Job Number: R0243983 Location: Arlington,VA,US Share job via: Share Data Scientist ... Experience building and applying machine learning techniques * Ability to travel up to 25% of the ...

rockITdata

Full Stack Data Scientist

Arlington, VA · On-site +1

This is a Remote position. Key Responsibilities * Data Collection and Preprocessing: * Develop ... Integrate data science workflows with existing systems and applications to enable seamless data ...

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Remote Building Science information

What is a remote building science professional?

A Remote Building Science professional is an expert who analyzes and improves building performance, energy efficiency, and occupant comfort—often using digital tools and remote technologies. They may conduct virtual assessments, review building plans, model energy use, and recommend improvements, all without being physically present at the project site. Their work helps ensure buildings are safe, healthy, and environmentally sustainable, using a mix of engineering, architecture, and environmental science principles. This role is particularly important for organizations seeking to optimize buildings across multiple locations or during times when in-person site visits are challenging.

What are the key skills and qualifications needed to thrive as a remote building science professional?

A strong foundation in building physics, energy modeling, and construction principles, often supported by a degree in engineering, architecture, or a related field, is essential for a Remote Building Science Specialist. Familiarity with technical tools such as energy simulation software (e.g., EnergyPlus, WUFI), CAD programs, and certifications like LEED or BPI is typically required. Outstanding analytical thinking, communication, and self-motivation are crucial soft skills, especially when collaborating remotely with multidisciplinary teams. These skills ensure accurate assessments, effective solutions, and successful project outcomes in the evolving field of sustainable building design and performance.

How does a remote building science professional typically collaborate with on-site teams during a project?

Remote Building Science professionals often work closely with on-site teams by leveraging digital tools such as video conferencing, BIM software, and cloud-based documentation. They are responsible for analyzing building performance data, providing recommendations, and ensuring that sustainability and energy efficiency goals are met. Effective communication and regular virtual meetings are key to maintaining alignment between remote experts and field personnel. Establishing clear protocols for data sharing and feedback helps ensure smooth collaboration throughout the project lifecycle.

What is the difference between Remote Building Science vs Remote Building Envelope Specialist?

AspectRemote Building ScienceRemote Building Envelope Specialist
CredentialsBuilding science certifications, LEED, HVAC knowledgeBuilding science background, certifications in envelope systems
Work EnvironmentConsulting, research, project analysis remotely or on-siteDesign, assessment, and troubleshooting building envelopes remotely or on-site
Industry UsageBuilding consulting firms, energy efficiency projectsArchitectural firms, construction, retrofit projects
Search & ComparisonOften compared for building performance rolesCompared for envelope design and repair roles

Remote Building Science and Remote Building Envelope Specialist roles share overlapping skills in building performance and certifications. However, Building Science focuses broadly on overall building systems and energy efficiency, while Building Envelope Specialists concentrate specifically on the building's exterior and envelope systems. Both roles are vital in construction and retrofit projects, often working together to improve building performance remotely.

What are the most commonly searched types of Building Science jobs in Virginia?

The most popular types of Building Science jobs in Virginia are:

What are popular job titles related to Remote Building Science jobs in Virginia?

For Remote Building Science jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Remote Building Science jobs in Virginia look for?

The top searched job categories for Remote Building Science jobs in Virginia are:

What cities in Virginia are hiring for Remote Building Science jobs?

Cities in Virginia with the most Remote Building Science job openings:

Remote Building Science jobs near you
Infographic showing various Remote Building Science job openings in Virginia as of August 2026, with employment types broken down into 78% Full Time, 20% Part Time, and 2% Contract. Highlights an 78% Physical, 3% Hybrid, and 19% Remote job distribution.

Graph Data Scientist (Fraud Analytics & Investigative Support)

Praescient Analytics

Fairfax, VA • On-site, Remote

Full-time

Retirement, PTO

Re-posted 24 days ago

Job description

Location: Remote (Occasional Travel May Be Required)
Clearance: Ability to obtain and maintain a Public Trust
Position Overview
Praescient Analytics is seeking an experienced Graph Data Scientist to develop advanced graph analytics that uncover hidden relationships, organized fraud networks, synthetic identities, and other complex patterns supporting federal fraud detection and investigative missions. This individual will leverage graph databases, graph algorithms, and machine learning techniques to transform large, interconnected datasets into actionable intelligence for investigators, analysts, and oversight organizations.
The ideal candidate is a hands-on technical specialist with deep expertise in graph theory, Neo4j, and graph-based machine learning. They thrive on solving complex network problems, building scalable graph data models, and discovering non-obvious relationships that traditional analytics cannot detect.
Key Responsibilities
  • Design, develop, and maintain graph-based analytic solutions supporting fraud detection, investigative analysis, and program integrity initiatives.
  • Build and optimize graph databases, graph schemas, and knowledge graphs using Neo4j or comparable graph database technologies.
  • Develop graph queries using Cypher or similar graph query languages to identify hidden relationships, fraud rings, suspicious networks, synthetic identities, and other complex entity relationships.
  • Apply graph algorithms, statistical analysis, and machine learning techniques to identify emerging fraud patterns and anomalous network behavior.
  • Design graph data models and scalable graph data pipelines that integrate structured and unstructured data from multiple public, non-public, commercial, and law enforcement data sources.
  • Perform network analysis utilizing centrality measures, community detection, shortest path algorithms, clustering, and graph-based anomaly detection techniques.
  • Collaborate with Data Engineers, Data Scientists, Investigative Analysts, and Technical Analytics Managers to integrate graph analytics into broader fraud detection models.
  • Validate graph analytic outputs, document methodologies, and ensure graph models are accurate, explainable, and reproducible.
  • Develop visualizations and relationship analyses that support investigative lead generation, case development, and executive briefings.
  • Support continuous improvement of graph analytics capabilities through experimentation with emerging graph technologies, graph machine learning techniques, and knowledge graph methodologies.

Required Qualifications
  • Must have experience with Fraud Analysis
  • Three (3) or more years of hands-on experience developing graph analytics using Neo4j or a comparable graph database platform.
  • Demonstrated fluency in Cypher or a comparable graph query language.
  • Strong understanding of graph theory and network analytics, including network topology, centrality measures, community detection, shortest path algorithms, graph clustering, and graph traversal techniques.
  • Three (3) or more years of hands-on experience applying statistical analysis, machine learning, clustering, classifiers, and anomaly detection techniques to graph-structured data.
  • Three (3) or more years of experience applying graph methods to fraud detection, relationship discovery, link analysis, and knowledge graph development.
  • Experience designing graph data models, graph schemas, and graph data pipelines supporting large-scale, high-complexity datasets.
  • Strong Python programming skills utilizing standard machine learning libraries and data science frameworks.
  • Excellent written and verbal communication skills with the ability to explain complex technical concepts to both technical and non-technical audiences.

Preferred Qualifications
Preference will be given to candidates with demonstrated experience in one or more of the following areas:
  • Applying graph analytics to fraud detection, fraud prevention, financial crime investigations, program integrity, anti-money laundering (AML), or other complex investigative environments.
  • Developing graph solutions supporting federal benefit programs, emergency relief initiatives, financial assistance programs, healthcare fraud, unemployment insurance fraud, grants management, or other high-volume public-sector programs.
  • Building knowledge graphs that integrate multiple public, non-public, commercial, financial, and law enforcement data sources into unified entity networks.
  • Detecting organized fraud rings, synthetic identities, shell companies, nominee entities, shared addresses, common bank accounts, related businesses, and other non-obvious relationships through graph analytics.
  • Designing and optimizing graph data pipelines, graph schemas, graph indexing strategies, and graph performance for enterprise-scale analytics environments.
  • Applying graph data science algorithms including PageRank, Louvain community detection, connected components, similarity algorithms, node embeddings, graph embeddings, link prediction, and graph-based anomaly detection.
  • Developing graph analytics within cloud-native environments utilizing Neo4j, Azure Databricks, Microsoft SQL Server, Azure Data Lake, Microsoft Fabric, Power BI, Git repositories, or Lakehouse architectures.
  • Leveraging Python libraries such as NetworkX, Neo4j Graph Data Science (GDS), Pandas, Scikit-learn, PyTorch Geometric, or comparable graph analytics and machine learning frameworks.
  • Supporting Offices of Inspector General (OIGs), law enforcement organizations, intelligence organizations, financial crime investigations, or other government oversight missions.
  • Developing interactive graph visualizations, relationship maps, and investigative link analysis products that accelerate lead generation, case development, and investigative decision-making.

What We're Looking For
We're looking for someone who sees relationships where others see disconnected data. The ideal candidate enjoys solving complex network problems, discovering hidden fraud patterns, and transforming interconnected datasets into actionable investigative intelligence. They combine strong graph theory fundamentals with practical engineering skills to build scalable graph analytics that help investigators identify organized fraud networks, prioritize investigative leads, and uncover relationships that would otherwise remain hidden.
What you can expect from us:
  • Real opportunity for career growth in an environment where your achievements will be celebrated
  • Constant collaboration with numerous teams to ensure client success
  • A team that respects and embraces your ideas and expertise
  • Coworkers that are motivated by pursuing excellence, rather than the prospect of personal gain
  • A workplace dedicated to supporting and bettering public safety and government agencies

Benefits:
  • Competitive salary based on qualifications and experience
  • Comprehensive, Company paid healthcare for you (We pay your premiums and deductibles)
  • 401(k) with company match
  • Travel & performance incentives
  • 3 weeks paid time off (plus Federal Holidays)
  • $5K annual training allowance
  • $500 book allowance
  • Tuition reimbursement program

Praescient Analytics is an Equal Employment Opportunity employer. Employment decisions are based on merit, qualifications, experience, performance, business needs, and applicable contract requirements. Praescient does not unlawfully discriminate or provide disparate treatment based on race, ethnicity, color, religion, sex, national origin, age, disability, veteran status, genetic information, or any other status protected by applicable law.
Praescient Analytics acknowledges the applicable clause and provision updates implementing Executive Order 14398, Addressing DEI Discrimination by Federal Contractors, and the related FAR/RFO updates, including FAR 52.222-90 where applicable. Praescient does not engage in racially discriminatory DEI activities, including disparate treatment based on race or ethnicity in recruitment, hiring, promotion, contracting, program participation, training, mentoring, leadership development, or allocation of company resources. Praescient's employment and contracting decisions are made based on merit, qualifications, experience, performance, business needs, and applicable contract requirements.
Applicants selected will be subject to a government security investigation and must meet eligibility requirements for access to classified information.
US Citizenship Required
Interested Candidates: Please forward your resume to recruiting@praescientanalytics.com and please visit our website to apply online at www.praescientanalytics.applicantstack.com/x/openings.

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