Senior Fraud Analytics Specialist
- Credit & Transactions RiverForest Connections Private Limited Atlanta, GA Job Details $100,000
- $120,000 an hour 16 hours ago Qualifications Payment Processing Systems Technical Proficiency Database software proficiency Full Job Description Senior Fraud Analytics Specialist
- Credit & Transactions About the Opportunity Join a high-growth fintech leader committed to expanding access to modern credit solutions across the United States.
Backed by decades of operational success and billions in managed consumer assets, our platform powers inclusive financial products across omnichannel retail, healthcare, and digital channels. We are seeking a data-driven Fraud Analytics Specialist to proactively safeguard our credit card portfolios, optimize real-time transaction monitoring, and design cutting-edge fraud mitigation strategies. Key Responsibilities Build, evaluate, and fine-tune machine learning and predictive fraud models to capture illicit activities across payment and card channels. Conduct surveillance on live transaction streams to detect anomalies, stop active threats, and minimize operational losses Perform deep-dive root cause investigations into complex fraud schemes, chargeback trends, and account takeovers to engineer preventative controls. Develop automated dashboards and dynamic executive reporting to monitor key risk indicators (KRIs), detection accuracy, and emerging fraud vectors. Partner cross-functionally with Engineering, InfoSec, Compliance, and Operations to embed fraud logic directly into platform workflows. Continuously evaluate new fraud attack vectors, digital payment patterns, and security tools to keep our defenses ahead of industry trends.
Required Qualifications & Skills Education :
Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Quantitative Finance, or a related field.
Experience :
5+ years in analytical risk or fraud roles, including at least 3 years directly focused on transaction monitoring and credit card fraud.
System Expertise :
Direct, hands-on experience utilizing Fiserv fraud platforms ( DefenseEdge or Advance Defense ).
Technical Stack :
Advanced proficiency in data querying and statistical modeling using SQL , Python , R , or SAS .
Domain Knowledge :
Thorough understanding of card networks, payment processing rails, e-commerce dispute cycles, and emerging cyber-fraud methodologies.
Communication :
Strong problem-solving aptitude with a proven track record of translating complex data insights into measurable risk reductions.
