Conduct deep-dive analytics into suspicious merchant behavior patterns to identify indicators of business impersonation, inauthentic merchants, dormant merchant take-over, and merchant account mule networks
Analyze merchant lifecycle behavior from onboarding through transaction activity
Quantity financial, operational, and reputational risk exposure
Produce structured analysis summaries for leadership and risk governance
Analyze rewards issuance, redemption, and transfer behaviors to detect points farming, promotion abuse, referral manipulation, account cycling, manufactured spend, and collusive merchant behavior
Identify emerging abuse typologies across travel, dining, retail, and digital marketplaces ecosystems
Quantify abuse rates, loss trends, and concentration risk
Deliver actional insights that inform rule creation and model features
Perform micro and macro-level portfolio analysis to detect behavioral shifts, coordinated abuse, cross-channel risk patterns, and geographic or fingerprint clustering
Use advanced SQL, Python, or analytical tooling to segment risk populations, identify anomaly cohorts, detect early indicators of exploitation
Develop recurrent reporting frameworks for emerging threats
Translate analytical findings into feature recommendations for model development, rule logic proposals, and threshold calibration insights
Partner with the Detection Team to provide validation support for new detection strategies
Develop defensible documentation of methodologies, findings, risk quantification logic and maintain structured playbooks
Ensure analytical outputs meet enterprise risk governance standards

Requirements

Bachelor’s Degree required in statistics, mathematics, physics, economics, or other analytical or quantitative discipline
3+ years experience in data analytics, fraud analytics, risk analytics, or financial crime analysis
Ability to translate data insights into risk strategy recommendations
Extensive experience working with Big Data environment with hands on coding experience within various traditional (SAS, SQL, etc.) and/or open source (i.e. Python, Impala, Hive, etc.) tools
Traditional and advanced machine learning techniques and algorithms, such as Logistic Regression, Gradient Boosting, Random Forests, etc.
Data visualization tools, such as Tableau
Excellent quantitative and analytic skills; ability to derive patterns, trends and insights, and perform risk/reward trade-off analysis
Ability to build effective presentations to communicate analytical findings to a wide array of audiences
Merchant risk or onboarding fraud analytics
Knowledge of fraud typologies such as account takeover, promotion exploitation, synthetic identities, and digital ecosystem abuse
Analytic rigor and intellectual curiosity
Pattern recognition across fragmented data sets
Strong written and verbal executive communication skills
Effective cross-functional project, resource, and stakeholder engagement and management, with ability to effectively drive collaboration across teams

Benefits

medical, dental & vision coverage
401(k)
life, accident, and disability insurance
wellness programs
paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data analyticsfraud analyticsrisk analyticsfinancial crime analysisSQLPythonmachine learningdata visualizationLogistic RegressionGradient Boosting

Soft Skills

quantitative skillsanalytical skillspresentation skillscommunication skillscollaborationintellectual curiositypattern recognitionproject managementstakeholder engagementresource management