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Remote Investigative Analyst Jobs (NOW HIRING)

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Fraud Investigations Analyst

Mclean, VA · On-site +1

Certain roles -- such as field-based sales or other remote-by-design positions -- may have ... investigative workflows Preferred Qualifications * Experience working at a fintech company ...

Kentech Consulting Inc

LEAD INVESTIGATOR: W2

Miami, FL · Remote

$45K/yr

This is a full-time, W-2 remote position ideal for professionals with strong investigative, research, and writing skills. As a remote investigator, you will analyze applications, conduct interviews ...

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Remote Investigative Analyst information

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$85.1K

$99.5K

How much do remote investigative analyst jobs pay per year?

As of Jul 22, 2026, the average yearly pay for remote investigative analyst in the United States is $85,141.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,000.00 and $98,500.00 per year, depending on experience, location, and employer.

What is the difference between Remote Investigative Analyst vs Remote Fraud Analyst?

AspectRemote Investigative AnalystRemote Fraud Analyst
Required CredentialsTypically requires investigative certifications, security clearances, or related degreesOften requires certifications like ACFE, fraud examination, or related degrees
Work EnvironmentConducts investigations remotely or on-site, often in law enforcement or private investigation settingsPrimarily remote, analyzing data and reports to detect fraudulent activities
Employer & Industry UsageUsed by law enforcement, private investigation firms, and corporate securityCommonly employed by financial institutions, insurance companies, and e-commerce firms

Remote Investigative Analysts focus on conducting investigations into various issues, including criminal activities or security breaches, often requiring investigative skills and certifications. Remote Fraud Analysts specialize in detecting and preventing fraud, primarily analyzing data remotely to identify suspicious activities. While both roles involve investigation and analysis, their focus areas and typical employers differ, with the Investigative Analyst more aligned with law enforcement and security, and the Fraud Analyst centered on financial and transactional fraud detection.

What does a Remote Investigative Analyst do?

A Remote Investigative Analyst is responsible for researching, analyzing, and interpreting data to support investigations, typically from a remote location. Their work may include reviewing documents, gathering intelligence, identifying patterns, and preparing detailed reports for law enforcement, corporate security, or government agencies. They use various databases and analytical tools to detect fraud, assess risks, or uncover suspicious activities. Working remotely allows them to collaborate online with team members and clients across different locations.

How does a Remote Investigative Analyst typically collaborate with team members despite working remotely?

Remote Investigative Analysts frequently collaborate with colleagues and stakeholders through secure digital communication platforms like video conferencing, encrypted emails, and project management tools. Regular virtual meetings are held to discuss ongoing cases, share findings, and coordinate next steps. Despite the physical distance, teamwork and information-sharing remain essential, with clear documentation and frequent check-ins ensuring that everyone stays aligned on objectives and deadlines. This collaborative environment helps analysts leverage diverse expertise and ensures thorough, accurate investigations.

What are the key skills and qualifications needed to thrive as a Remote Investigative Analyst, and why are they important?

To thrive as a Remote Investigative Analyst, you typically need strong analytical skills, attention to detail, and a background in fields such as criminal justice, finance, or cybersecurity. Familiarity with data analysis tools, case management systems, and relevant certifications (like CFE or CAMS) is often required. Excellent written communication, critical thinking, and discretion are crucial soft skills for gathering, interpreting, and reporting sensitive information effectively. These competencies enable analysts to uncover accurate insights, ensure compliance, and support organizational decision-making from a remote environment.
More about Remote Investigative Analyst jobs
What cities are hiring for Remote Investigative Analyst jobs? Cities with the most Remote Investigative Analyst job openings:
What are the most commonly searched types of Investigative Analyst jobs? The most popular types of Investigative Analyst jobs are:
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What job categories do people searching Remote Investigative Analyst jobs look for? The top searched job categories for Remote Investigative Analyst jobs are:
Remote Investigative Analyst jobs near you
Infographic showing various Remote Investigative Analyst job openings in the United States as of July 2026, with employment types broken down into 82% Full Time, 6% Part Time, 6% Temporary, and 6% Contract. Highlights an 100% Remote job distribution, with an average salary of $85,141 per year, or $40.9 per hour.
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 26 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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