Data Analyst – Merchant and Loyalty Rewards Integrity, AVP

Role Overview

• 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

Tech Stack

• Open Source
• Python
• SQL
• Tableau

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