About the Role
The Fraud Data Analyst serves as a trusted advisor and analytical consultant, supporting large banking, financial services, and e-commerce clients in the implementation, optimization, and ongoing management of fraud and identity solutions. This role blends data analytics, fraud investigation, technical consulting, and client engagement.
The ideal candidate is highly analytical, customer-focused, and passionate about solving complex fraud and risk challenges. You will work closely with clients to assess fraud patterns, analyze performance data, develop detection strategies, and provide actionable recommendations that improve fraud prevention outcomes while minimizing friction for legitimate customers.
Responsibilities
Review suspicious activity, fraud alerts, and complex fraud cases to identify emerging risks, trends, and vulnerabilities.
Analyze large datasets to build, optimize, and maintain fraud detection rules, models, and workflows that improve fraud prevention and operational efficiency.
Conduct root cause analyses to investigate client issues, identify contributing factors, and develop effective solutions.
Deliver impactful analyses and recommendations to both technical and non-technical stakeholders through presentations, reports, and client consultations.
Advise clients on best practices for implementing and optimizing ThreatMetrix® Digital Identity Network, Digital Device Profiling (DDP), and other LexisNexis Risk Solutions products.
Partner with clients to improve fraud mitigation strategies while balancing customer experience and operational objectives.
Perform proof-of-concept analyses and demonstrate measurable business value through data-driven insights.
Collaborate closely with fraud managers, risk analysts, developers, project managers, and other customer stakeholders to support solution adoption and performance optimization.
Build and maintain strong client relationships by serving as a trusted consultant and subject matter expert.
Develop reports, dashboards, and analytical deliverables using SQL, Excel, Python, R, and other analytics tools.
Research emerging fraud techniques, cybersecurity trends, and financial crime risks to provide proactive recommendations.
Support internal teams with product feedback, solution enhancements, and client-related insights.
Requirements
Required Qualifications
Bachelor's degree in a quantitative, technical, analytical, or related field.
3+ years of experience supporting customer-facing technology, analytics, or fraud prevention solutions.
3+ years of experience working within financial services, banking, fintech, payments, risk management, or e-commerce environments.
3+ years of experience performing advanced analytics using Python (including pandas, scikit-learn, and matplotlib), R, or similar analytical tools.
Strong proficiency with SQL and Excel for data analysis, reporting, and performance monitoring.
Experience investigating fraud, financial crimes, or suspicious activity, including account takeover, card-not-present fraud, money laundering, and social engineering attacks.
Demonstrated ability to perform root cause analysis and solve complex business and technical challenges.
Strong communication and presentation skills with the ability to translate technical findings into business-focused recommendations.
Proven ability to manage multiple priorities in a fast-paced, client-facing environment.
Strong customer service mindset with a passion for helping clients achieve measurable business outcomes.
Preferred Qualifications
Experience with fraud prevention, digital identity, authentication, risk management, or financial crime solutions.
Knowledge of cybersecurity concepts, including browser fingerprinting, device intelligence, public key infrastructure (PKI), computer networking, and device authentication.
Experience with ThreatMetrix® solutions or similar fraud and identity platforms.
Familiarity with machine learning, predictive analytics, statistical modeling, scorecard development, or graph analytics.
Experience developing fraud detection strategies and risk-based decisioning frameworks.
Knowledge of AML, KYC, fraud operations, and regulatory compliance practices.
Experience working directly with enterprise banking, financial services, or large e-commerce organizations.
Ability to influence client strategy and serve as a trusted advisor across both business and technical teams.


U.S. National Base Pay Range: $104,900 - $174,700. Geographic differentials may apply in some locations to better reflect local market rates.

If performed in New York, the base pay range is $115,400 - $192,200.If performed in New York City, the base pay range is $125,900 - $209,700.If performed in Rochester, NY, the base pay range is $104,900 - $174,700.

This job is eligible for an annual incentive bonus.





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