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How much do fraud detection machine learning jobs pay per hour?

As of Sep 3, 2026, the average hourly pay for fraud detection machine learning in Silver Spring, MD is $18.66, according to ZipRecruiter salary data. Most workers in this role earn between $15.38 and $19.90 per hour, depending on experience, location, and employer.

What is fraud detection using machine learning?

Fraud detection using machine learning involves leveraging algorithms and data analysis techniques to identify suspicious or fraudulent activities in various domains, such as banking, e-commerce, or insurance. These systems analyze large volumes of transaction data to detect patterns or anomalies that may indicate fraud. Machine learning models can adapt over time, improving their accuracy as they are exposed to more data. This approach helps organizations automate and enhance their ability to prevent, detect, and respond to fraudulent behavior efficiently.

What are some common challenges faced by professionals working in fraud detection machine learning, and how can they be addressed?

Professionals in Fraud Detection Machine Learning often face challenges such as dealing with highly imbalanced datasets, rapidly evolving fraud patterns, and the need for real-time detection. Managing data imbalance requires careful selection of evaluation metrics and specialized algorithms. Staying ahead of new fraud tactics involves continuous model retraining and close collaboration with domain experts. Additionally, integrating machine learning solutions with existing systems often requires cross-functional teamwork with IT, security, and compliance teams.

What are the key skills and qualifications needed to thrive as a fraud detection machine learning specialist, and why are they important?

To thrive as a Fraud Detection Machine Learning Specialist, you need strong expertise in machine learning, statistical analysis, and programming languages like Python or R, typically supported by a degree in computer science, data science, or a related field. Familiarity with tools such as TensorFlow, Scikit-learn, SQL databases, and experience with big data platforms or cloud services is highly valuable. Critical thinking, attention to detail, and effective communication are crucial soft skills for identifying complex fraud patterns and collaborating with interdisciplinary teams. These competencies are vital for developing accurate models that protect organizations from financial losses and maintain trust with customers.

What is the difference between Fraud Detection Machine Learning vs Fraud Analyst?

AspectFraud Detection Machine LearningFraud Analyst
CredentialsData science, machine learning certifications, programming skillsFinance, criminal justice degrees, analytical skills
Work EnvironmentData-driven, tech-focused, often in financial or e-commerce sectorsInvestigative, report-focused, in financial institutions or insurance companies
Employer & IndustryTech companies, banks, e-commerce platformsFinancial institutions, insurance firms, retail

Fraud Detection Machine Learning involves developing algorithms to identify fraudulent activities automatically, relying heavily on data analysis and programming. Fraud Analysts manually investigate suspicious cases and interpret data insights. While both roles aim to prevent fraud, Machine Learning specialists focus on building models, whereas Fraud Analysts focus on case investigation and decision-making.

What are popular job titles related to Fraud Detection Machine Learning jobs in Silver Spring, MD?

For Fraud Detection Machine Learning jobs in Silver Spring, MD, the most frequently searched job titles are:

What cities near Silver Spring, MD are hiring for Fraud Detection Machine Learning jobs?

Cities near Silver Spring, MD with the most Fraud Detection Machine Learning job openings:

Fraud Detection Machine Learning jobs near you
Infographic showing various Fraud Detection Machine Learning job openings in Silver Spring, MD as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $38,816 per year, or $18.7 per hour.

Graph Data Scientist (Fraud Analytics & Investigative Support)

Praescient Analytics

Fairfax, VA • On-site

$90 - $120/hr

Other

Retirement, PTO

Re-posted 8 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
  • 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.

US Citizenship Required

Applicants selected will be subject to a government security investigation and must meet eligibility requirements for access to classified information.

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