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Software Engineer Fraud Detection Jobs in Virginia

McAfee Corp.

Fraud Analyst

Reston, VA · On-site

You will work closely with payments, risk, marketing, data, and engineering teams to ensure the ... software. * Experience with fraud detection vendors (e.g. Signifyd, Bolster, Arkose, others)

ID.me

Senior Fraud Investigations Analyst

Mclean, VA · On-site

Partner with engineering, data, and product teams to enhance fraud detection capabilities and signal quality * Act as an escalation point for high-severity or ambiguous fraud cases * Develop and ...

Booz Allen Hamilton, Inc.

Data Engineer

Arlington, VA · On-site

$62K - $141K/yr

... fraud detection to cancer research to national intelligence. As a Data Engineer at Booz Allen, you ... You Have: * 2+ years of experience writing software in programming languages, including Python * 2+ ...

Booz Allen Hamilton

Data Engineer

Arlington, VA · On-site

$62K - $141K/yr

... fraud detection to cancer research to national intelligence. As a Data Engineer at Booz Allen, you ... You Have: * 2+ years of experience writing software in programming languages, including Python * 2+ ...

Elder Research

Senior Data Engineer

Arlington, VA · Hybrid

$122K - $165K/yr

We are seeking a Senior Data Engineer to support the infrastructure and pipelines powering mission-critical fraud detection analytics. This role ensures reliable integration, execution, and ...

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Software Engineer Fraud Detection information

What does a Software Engineer in Fraud Detection do?

A Software Engineer in Fraud Detection designs and develops systems to identify and prevent fraudulent activities within digital platforms, such as banking or e-commerce environments. They build algorithms to analyze user behavior, detect anomalies, and flag suspicious transactions in real time. Their work often involves machine learning, big data analysis, and close collaboration with data scientists and security teams to continuously improve fraud detection accuracy. These engineers play a key role in protecting businesses and customers from financial loss and cybercrime.

What is the difference between Software Engineer Fraud Detection vs Data Scientist Fraud Detection?

AspectSoftware Engineer Fraud DetectionData Scientist Fraud Detection
Required CredentialsBachelor's in CS or related field, programming skillsBachelor's or higher in CS, Statistics, or Data Science
Work EnvironmentDevelops fraud detection systems, writes code, implements algorithmsAnalyzes data, builds models, interprets results
Employer & Industry UsageFinancial institutions, fintech, e-commerceFinancial services, tech companies, insurance
Common Search & ComparisonFocuses on software development for fraud detectionFocuses on data analysis and modeling for fraud detection

While both roles work in fraud detection, Software Engineer Fraud Detection primarily develops and maintains detection systems through coding, whereas Data Scientist Fraud Detection analyzes data and builds models to identify fraudulent activity. Both roles often collaborate but differ in their core focus and skill sets.

What are the key skills and qualifications needed to thrive as a Software Engineer in Fraud Detection, and why are they important?

To thrive as a Software Engineer in Fraud Detection, strong programming skills (such as Python, Java, or Scala), a solid understanding of algorithms, data structures, and experience with machine learning or statistical analysis are generally required, often supported by a degree in computer science or a related field. Familiarity with big data platforms (like Hadoop or Spark), real-time analytics systems, and fraud detection tools or frameworks is typically expected. Analytical thinking, problem-solving abilities, and effective communication are key soft skills that differentiate top performers in this field. These skills are crucial for developing robust systems that can quickly identify and prevent fraudulent activities, protecting both users and organizations.

How does a Software Engineer in Fraud Detection typically collaborate with data scientists and analysts to identify fraudulent activity?

Software Engineers in Fraud Detection work closely with data scientists and analysts to build, refine, and deploy systems that detect and prevent fraud. While data scientists may develop models and identify patterns from large datasets, engineers are responsible for integrating these models into scalable, real-time systems within the company's technology stack. Regular communication and joint problem-solving are essential, as engineers must understand the logic behind models and analysts' findings to ensure accurate implementation and continuous improvement. This collaborative environment helps create robust fraud detection mechanisms that adapt to evolving threats.
What are popular job titles related to Software Engineer Fraud Detection jobs in Virginia? For Software Engineer Fraud Detection jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Software Engineer Fraud Detection jobs in Virginia look for? The top searched job categories for Software Engineer Fraud Detection jobs in Virginia are:
What cities in Virginia are hiring for Software Engineer Fraud Detection jobs? Cities in Virginia with the most Software Engineer Fraud Detection job openings:
Software Engineer Fraud Detection jobs near you
Senior Fraud Investigations Analyst

Senior Fraud Investigations Analyst

ID.me

Mclean, VA

Other

Posted 26 days ago

ID.me rating

6.3

Company rating: 6.3 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

164th of 186 rated software companies

Job description

Senior Fraud Investigations AnalystRole Overview

ID.me is looking for a Senior Fraud Investigations Analyst to join our organization as an execution-focused individual contributor. This role is responsible for leading investigations into account takeover (ATO) activity, identifying fraud patterns at scale, and strengthening detection strategies.

We are seeking a highly skilled analyst who can independently analyze large datasets, uncover coordinated fraud campaigns, and drive improvements in detection systems. This role requires moving beyond case-by-case investigation into trend analysis, signal development, and data-driven decision making.

This role is based out of our McLean, VA office and requires full-time in-office attendance.

Responsibilities
  • Lead complex account takeover (ATO) investigations, including multi-account and organized fraud activity
  • Analyze large datasets using SQL, Python, or similar tools to identify trends, anomalies, and attack patterns
  • Use fraud detection tools, machine learning outputs, and risk-scoring systems to drive high-quality investigations
  • Independently own investigations from detection through resolution, including clear documentation and recommendations
  • Identify gaps in detection logic and contribute to improvements in rules, models, and workflows
  • Partner with engineering, data, and product teams to enhance fraud detection capabilities and signal quality
  • Act as an escalation point for high-severity or ambiguous fraud cases
  • Develop and refine investigation playbooks and analytical approaches for ATO detection
  • Support reporting and analysis of fraud trends, detection performance, and operational metrics
  • Leverage AI/LLM tools to analyze large datasets, identify fraud patterns, and improve investigation speed and accuracy while validating outputs against source data
Basic Qualifications
  • Bachelor's degree from an accredited institution; strong preference for quantitative fields such as Economics, Computer Science, Statistics, Mathematics, or similar
  • 4+ years of experience in fraud investigations, threat intelligence, cybersecurity, or risk management, with a focus on account takeover (ATO) attacks (typically 4-8 years total experience; candidates should have <10 years overall)
  • 2+ years of experience using SQL, Python, or similar tools to analyze data and drive investigations
  • 2+ years of hands-on experience using fraud detection tools, machine learning models, or risk-scoring methodologies
  • 2+ years of experience interpreting fraud indicators, behavioral signals, or transaction monitoring data
  • Demonstrated experience analyzing fraud trends beyond individual case investigations (e.g., pattern detection, ring analysis)
  • Experience using AI/LLM tools to enhance data analysis and investigations, with demonstrated ability to validate and apply outputs effectively
Preferred Qualifications
  • Experience at a fintech company, technology company, or reputable financial institution 
  • Strong experience analyzing organized fraud rings or large-scale ATO campaigns
  • Experience influencing fraud detection logic, models, or rule systems
  • Familiarity with AI/ML techniques applied to fraud detection and risk analysis
  • Ability to translate analytical findings into actionable improvements for product, engineering, or risk systems

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