Senior Fraud Investigations Analyst

About the role

This role leads fraud investigations and detection improvements for ID.me's digital identity platform. The position is based full-time in-office at McLean, VA.

Analysts turn data into clear answers. They pull numbers, clean data, build dashboards, and explain what changed and why. The work supports decisions across sales, product, marketing, and operations. Strong analysts pair technical skill with business curiosity. Analysts are the bridge between data and decisions. Most analysts own recurring reports and are expected to improve them over time.

Key facts

What you'll do

Investigations are owned independently from detection through resolution, with clear documentation and recommendations to ensure consistent handling of fraud cases and support measurable improvements in resolution rates.

High-severity or ambiguous fraud cases are escalated to provide timely guidance and support coordinated responses across risk and operations teams.

Requirements

Bachelor's degree from an accredited institution; quantitative fields such as Economics, Computer Science, Statistics, Mathematics, or similar are strongly preferred.

4+ years of experience in fraud investigations, threat intelligence, cybersecurity, or risk management, with a focus on account takeover (ATO) attacks; typical total experience is 4-8 years.

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, such as pattern detection and ring analysis.

Experience using AI/LLM tools to enhance data analysis and investigations, with demonstrated ability to validate and apply outputs effectively.

Nice to have

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.

Practical notes

This role requires full-time in-office work at one of our offices, primarily McLean, VA, unless otherwise noted for specific duties.

Typical interview steps

Analyst interviews often include a SQL or spreadsheet exercise, a case question, and behavioral rounds. Candidates may be asked to analyze a dataset, define a metric, or estimate an outcome. Presenting findings clearly is tested as often as the analysis itself. Interviewers often test speed and clarity with a timed exercise. Explaining what the numbers mean, not just what they are, is the differentiator.

Good to know

The role relies on data analysis, fraud detection tools, and emerging technologies such as AI/LLM systems to investigate and prevent fraud.

The position balances independent investigation ownership with structured playbooks and cross-functional partnerships.

General career guidance and professional development are supported through learning and development benefits available at the company.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Analyst careers can grow into senior analyst, analytics manager, or data science roles. Some analysts move into product or business operations. Deeper technical skills or broader business ownership are the two main paths. Analyst roles are a common on-ramp into product, marketing, or operations leadership. Building deep domain knowledge alongside analytics is the fastest path.