Job Description

Senior Fraud Investigations Analyst

McLean, Virginia

Company Overview

ID.me is the next-generation digital identity wallet that simplifies how individuals securely prove their identity online. Consumers can verify their identity with ID.me once and seamlessly login across websites without having to create a new login and verify their identity again. Over 152 million users experience streamlined login and identity verification with ID.me at 20 federal agencies, 45 state government agencies, and 70+ healthcare organizations. More than 600+ consumer brands use ID.me to verify communities and user segments to honor service and build more authentic relationships. ID.me's technology meets the federal standards for consumer authentication set by the Commerce Department and is approved as a NIST 800-63-3 IAL2 / AAL2 credential service provider by the Kantara Initiative. ID.me is committed to "No Identity Left Behind" to enable all people to have a secure digital identity.

Role 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 48 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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