Matrix IFS is seeking a Senior Fraud Risk Analyst to join our Customer Success organization . This is a high‑impact, technical, and client‑facing role , supporting major financial institutions, merchants, and enterprise clients in leveraging our machine‑learning–driven fraud and financial crime platform . This role sits at the intersection of Fraud Strategy, Data Science, and Client Delivery . You will act as a strategic interpreter—translating complex analytical and model outputs into actionable business decisions, while converting ambiguous client fraud challenges into clear technical requirements for internal product, engineering, and data teams. This position is ideal for a senior fraud professional who can lead strategy workshops , own client conversations , and drive implementations , not just advise. Fraud Strategy & Domain Leadership Act as a subject‑matter expert across Fraud and Digital Trust domains, internally and externally Lead fraud risk strategy workshops and executive‑level client discussions Translate fraud trends, analytics, and model outputs into practical business and risk decisions Drive benchmarking, operational strategy, and fraud best practices across clients Provide industry insight into fraud typologies, compliance considerations, and emerging risk trends Interpret machine‑learning model outputs, detection signals, and analytics Convert complex data science results into clear, actionable fraud logic Identify anomalies and high‑risk patterns using SQL and analytical tools Translate fraud patterns into structured technical requirements for engineering, product, and data teams Proactively identify model blind spots and propose mitigation strategies Client Delivery & Implementation Support and execute fraud platform implementations (not advisory‑only) Optimize fraud strategies including: Detection rules Thresholds and scoring Operational workflows Balance strategic fraud analysis with hands‑on operational tuning Manage complex enterprise stakeholder relationships with confidence Influence, challenge, and push back professionally when required Cross‑Functional Collaboration Partner closely with Product, Research, Engineering, and Data Science teams Contribute to use‑case prioritization and product evolution Assist marketing and go‑to‑market teams with intelligence‑driven insights Deep Understanding of Modern Fraud Typologies Authorized Push Payment (APP) Account Takeover (ATO) Mule activity Digital and payment fraud across banking and fintech Ability to Connect Model Behavior to Real‑World Financial Crime Impact Ability to connect model behavior to real‑world financial crime impact. Analytical Rigor Comfortable finding high‑risk signals within large datasets Data‑driven approach to decision‑making and prioritization Operational Agility Comfortable switching between high‑level strategy and granular rule optimization Understands operational KPIs such as: False positives Detection rates Hit rates Client Management & Communication Extensive customer‑facing experience in enterprise environments Outstanding communication skills—able to explain: False positives Trade‑offs between risk and customer experience Ability to clearly translate technical risk into business loss impact Skills & Qualifications Technical Skills Strong SQL for investigative analysis and fraud pattern detection Experience with ML‑based and rule‑based detection systems Ability to write, refine, and operationalize fraud detection rules Understanding of: Data requirements Feature importance Monitoring and model limitations Ability to convert client fraud issues into clear technical use cases Strong understanding of: Fraud schemes across banking, payments, and digital channels Proven ability to lead risk strategy workshops Comfortable managing multiple stakeholders and parallel priorities #J-18808-Ljbffr