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Overnight Remote Machine Learning Jobs in Virginia

NT Concepts

Data Scientist

Herndon, VA · On-site +1

... the machine learning development lifecycle, from data curation and synthetic data generation to ... Herndon, VA with remote flexibility. Must be local to the DC Metro area. Responsibilities * Curate ...

ManTech

Solution Engineer

Herndon, VA · Remote

$179K - $318K/yr

This role heavily emphasizes structural data integrity, deep machine learning pipelines, and robust ... For Remote Opportunities), education and certifications as well as Federal Government Contract ...

Axiom Path

Senior Data Engineer #3624301

Richmond, VA · Remote

$114K - $227K/yr

... remote applicants residing in Eastern Standard Time states. The role offers the chance to work on modern data architecture, Lakehouse engineering, machine learning enablement, and production-grade ...

Ardent Eagle Solutions

Senior Data Engineer

Arlington, VA · On-site +1

$135K - $205K/yr

Collaborate closely with Data Scientists to optimize infrastructure supporting machine learning ... Location and Work Hours * 100% Remote (United States) * Standard operating hours between 6:00 AM ...

Ardent Eagle Solutions

Senior Data Engineer

Arlington, VA · On-site +1

$135K - $205K/yr

Collaborate closely with Data Scientists to optimize infrastructure supporting machine learning ... Location and Work Hours * 100% Remote (United States) * Standard operating hours between 6:00 AM ...

Ardent Eagle Solutions

Senior Data Engineer

Arlington, VA · On-site +1

$135K - $205K/yr

Collaborate closely with Data Scientists to optimize infrastructure supporting machine learning ... Location and Work Hours * 100% Remote (United States) * Standard operating hours between 6:00 AM ...

GRVTY

Software Engineer, Senior

Herndon, VA · On-site +1

$126K - $166K/yr

Develop and integrate machine learning workflows - including training data preparation, model ... Background in signal processing, image processing, or remote sensing data workflows. * Experience ...

GRVTY

Imagery Scientist (EO) - Senior

Falls Church, VA · Remote

$97K - $133K/yr

... Machine Learning algorithm testing and evaluation. The ideal candidate is an expert imagery ... Remote sensing phenomenology * Image formation processes * Exploitation products and methodologies

Team Velocity

Senior Data Engineer

Herndon, VA · Remote

$150K - $195K/yr

Familiarity with machine learning data pipelines and feature engineering concepts. * Experience working in a remote-first environment. COMPENSATION Compensation commensurate on experience.

Team Velocity

Senior Data Engineer

Herndon, VA · On-site +1

$150K - $195K/yr

Familiarity with machine learning data pipelines and feature engineering concepts. * Experience working in a remote-first environment. COMPENSATION Compensation commensurate on experience.

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All JobsMachine Learning JobsOvernight Remote Machine Learning JobsOvernight Remote Machine Learning Jobs in Virginia

Overnight Remote Machine Learning information

What are Overnight Remote Machine Learning jobs?

Overnight Remote Machine Learning jobs are positions where professionals work on machine learning tasks outside of traditional office hours, typically during the night, and do so from a remote location. These roles may involve building models, analyzing data, or maintaining machine learning systems while collaborating with teams in different time zones or providing 24/7 support. Overnight shifts can be critical for companies with global operations or those that require continuous system monitoring. Working remotely allows for flexibility and access to a wider talent pool. These positions often require strong programming and analytical skills, as well as the ability to work independently with minimal supervision.

What are the key skills and qualifications needed to thrive as an Overnight Remote Machine Learning Engineer, and why are they important?

To thrive as an Overnight Remote Machine Learning Engineer, you need strong programming skills (especially in Python), a solid background in statistics and algorithms, and a relevant degree in computer science or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch, experience with cloud platforms like AWS or GCP, and knowledge of version control systems are typically required. Excellent problem-solving abilities, self-motivation, and clear written communication are crucial soft skills for remote and overnight work schedules. These competencies ensure that you can efficiently develop, deploy, and monitor machine learning models independently while collaborating across time zones.

What are some common challenges faced by overnight remote machine learning professionals, and how can they be addressed?

Overnight remote machine learning professionals often encounter challenges like coordinating with daytime teams across different time zones, maintaining effective communication, and managing alertness during non-traditional hours. To address these, it's helpful to establish clear communication protocols, use collaboration tools for asynchronous updates, and set a structured sleep and work routine to ensure productivity. Additionally, leveraging automated monitoring and robust documentation helps in managing handoffs and reducing errors during shift changes.

What is the difference between Overnight Remote Machine Learning vs Data Scientist?

AspectOvernight Remote Machine LearningData Scientist
CredentialsBachelor's or higher in CS, ML, or related fields; certifications like AWS, TensorFlowBachelor's or higher in CS, Statistics, or related fields; advanced degrees common
Work EnvironmentRemote, overnight shifts, focused on model deployment and data pipelinesOffice or remote, standard hours, focused on data analysis and model development
Industry UsageTech, finance, healthcare companies with 24/7 operationsResearch, tech, consulting firms, often with flexible hours

Overnight Remote Machine Learning roles typically focus on deploying models and maintaining data pipelines during overnight hours, often requiring specific certifications and remote work setups. Data Scientists usually work during regular hours, concentrating on data analysis, model development, and research. Both roles are vital in tech-driven industries but differ mainly in work hours, environment, and focus areas.

What are the most commonly searched types of Remote Machine Learning jobs in Virginia? The most popular types of Remote Machine Learning jobs in Virginia are:
What are popular job titles related to Overnight Remote Machine Learning jobs in Virginia? For Overnight Remote Machine Learning jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Overnight Remote Machine Learning jobs in Virginia look for? The top searched job categories for Overnight Remote Machine Learning jobs in Virginia are:
Overnight Remote Machine Learning jobs near you
Infographic showing various Overnight Remote Machine Learning job openings in Virginia as of July 2026, with employment types broken down into 1% As Needed, 74% Full Time, 23% Part Time, 1% Temporary, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution.
Graph Data Scientist (Fraud Analytics & Investigative Support)

Graph Data Scientist (Fraud Analytics & Investigative Support)

Praescient Analytics

Fairfax, VA • On-site, Remote

Full-time

Retirement, PTO

Posted 11 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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