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Fraud Analytics Jobs in Washington (NOW HIRING)

Ardent

Cybersecurity Analytics Lead

Washington, DC

Support fraud analytics, predictive analytics, and forensic analysis activities across enterprise environments. * Conduct analysis of structured and unstructured data to identify suspicious or ...

FVCBANK

Fraud Analyst

Manassas, VA · On-site

$70K - $79K/yr

Fraud Analyst Departm ent(s): BSA Department / Manassas Rep orts to: VP Director / BSA & Fraud About Us: Since opening our doors in 2007, FVCbank has incorporated a vision of creating a positive and ...

ID.me

Fraud Investigations Analyst

Mclean, VA · On-site

Fraud Investigations Analyst Role Overview ID.me is looking for an Fraud Investigations Analyst to join our organization as an execution-focused individual contributor. This role centers on ...

ID.me

Fraud Investigations Analyst

Mclean, VA · On-site

Fraud Investigations Analyst Role Overview ID.me is looking for an Fraud Investigations Analyst to join our organization as an execution-focused individual contributor. This role centers on ...

Recorded Future

Fraud Analyst

Washington, DC · Remote

$78K - $117K/yr

We are looking for a Fraud Analyst to join our Threat Intelligence team! What You'll Do: * Conduct research on emerging cyber threats, adversary activity, and global threat trends * Collect, analyze ...

Recorded Future

Fraud Analyst

Washington, DC · On-site

$78K - $117K/yr

We are looking for a Fraud Analyst to join our Threat Intelligence team! What You'll Do: * Conduct research on emerging cyber threats, adversary activity, and global threat trends * Collect, analyze ...

Ardent

Senior Cybersecurity Program Manager

Washington, DC

$131K - $131K/yr

This role will oversee multidisciplinary fraud analytics and cybersecurity operations teams responsible for predictive analytics, forensic analysis, fraud detection, incident coordination, and ...

International Turbo Sytems Inc

Be Seen First

Senior Fraud Analyst

Washington, DC · On-site

$40 - $45/hr

Fraud Analyst, Senior Location: Washington, DC (SEC Headquarters) Work Schedule: Monday-Friday, 9:00 a.m.-5:30 p.m.; minimum two on-site days between Monday and Thursday Employment Type: Full-time, ...

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How much do fraud analytics jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for fraud analytics in Washington is $34.75, according to ZipRecruiter salary data. Most workers in this role earn between $23.94 and $38.37 per hour, depending on experience, location, and employer.

How does a fraud analytics professional typically collaborate with other departments within an organization?

Fraud Analytics professionals frequently work cross-functionally, partnering with teams such as IT, compliance, risk management, and customer service. They analyze data to identify suspicious activities and then communicate findings to relevant stakeholders, often participating in investigations or recommending process improvements. Effective collaboration ensures that fraud detection strategies stay up-to-date and align with broader organizational goals, making strong communication skills and teamwork essential for success in this role.

What is the difference between Fraud Analytics vs Fraud Prevention Specialist?

AspectFraud AnalyticsFraud Prevention Specialist
Primary FocusAnalyzing data to detect and predict fraudulent activitiesImplementing strategies and actions to prevent fraud
Skills & CertificationsData analysis, statistical tools, SQL, certifications like Certified Fraud Examiner (CFE)Customer service, risk management, fraud detection techniques, certifications like CFE
Work EnvironmentData analysis teams, financial institutions, tech companiesCustomer support centers, financial institutions, retail
GoalsIdentify patterns, develop models, improve detection accuracyReduce fraud incidents, enhance prevention measures

While both roles aim to combat fraud, Fraud Analytics focuses on analyzing data to identify and predict fraudulent activities, whereas Fraud Prevention Specialists implement measures to prevent fraud from occurring. Both roles often collaborate but serve different functions within fraud management strategies.

What are the key skills and qualifications needed to thrive as a fraud analytics professional, and why are they important?

To thrive in Fraud Analytics, you need strong analytical abilities, proficiency in statistics, and experience with data analysis, often supported by a degree in mathematics, statistics, computer science, or a related field. Familiarity with data mining tools, SQL, Python, machine learning platforms, and certifications like Certified Fraud Examiner (CFE) are typically required. Attention to detail, critical thinking, and effective communication are essential soft skills for interpreting data patterns and presenting findings to stakeholders. These skills are crucial for detecting fraudulent activities, minimizing financial risks, and supporting organizational integrity.

How much does a fraud analyst get paid?

A fraud analyst's salary typically ranges from $50,000 to $80,000 annually, depending on experience, location, and industry. Entry-level positions may start lower, while experienced analysts with certifications or specialized skills can earn higher salaries. Many roles also include benefits such as bonuses and professional development opportunities.

What is fraud analytics?

Fraud analytics is the process of using data analysis, statistical methods, and machine learning techniques to detect, prevent, and investigate fraudulent activities within an organization. Professionals in this field analyze large sets of transactional and behavioral data to identify patterns and anomalies that may indicate fraud. Fraud analytics is commonly used in industries such as banking, insurance, retail, and e-commerce to minimize financial losses and protect customers. The role often involves working with specialized software and collaborating with other teams to implement effective anti-fraud strategies.

Is fraud analysis a good career?

Fraud analysis is a growing field within risk management that involves detecting and preventing fraudulent activities using data analysis and investigative skills. It offers opportunities for advancement, requires knowledge of analytics tools, and often involves working in financial or e-commerce environments. The role can be stable and rewarding for those interested in security and data-driven decision making.
What are the most commonly searched types of Fraud Analytics jobs in Washington? The most popular types of Fraud Analytics jobs in Washington are:
What are popular job titles related to Fraud Analytics jobs in Washington? For Fraud Analytics jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Fraud Analytics jobs in Washington look for? The top searched job categories for Fraud Analytics jobs in Washington are:
What cities in Washington are hiring for Fraud Analytics jobs? Cities in Washington with the most Fraud Analytics job openings:
Fraud Analytics jobs near you
Infographic showing various Fraud Analytics job openings in Washington as of August 2026, with employment types broken down into 1% Internship, 92% Full Time, 3% Part Time, and 4% Contract. Highlights an 79% Physical, 5% Hybrid, and 16% Remote job distribution, with an average salary of $72,285 per year, or $34.8 per hour.

Graph Data Scientist (Fraud Analytics & Investigative Support)

Praescient Analytics

Fairfax, VA • On-site, Remote

Full-time

Retirement, PTO

Re-posted 14 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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