About the job

DataVisor is the world’s leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's solution scales infinitely and enables organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine and investigation tools work together to provide guaranteed performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!
Job Summary:As a Fraud Analytics Manager, (FAM), you are responsible for leveraging our state-of-art fraud detection SaaS products to solve our key clients' fraud and risk problems. You provide technical solutions and consultative services of our state-of-art fraud detection SaaS products to a portfolio of Fortune 500 customers in FinTech, Banking, E-commerce industries. From managing detection rules and models’ performance to providing recommendations on risk strategies and operations, you are an expert who can provide technical guidance, deep dive analysis, and share best practices with customers on using our Machine Learning models, Rules Engine, Device Intelligence Signals, and Case Manager to stay on top of fraud and risks. You work cross-functionally with Engineering, ML Modeling, and Product teams to implement new ideas to our analytics solution.
Job Location:This job is remote-based. Can be in the US or Canada. West Coast time zone required.
Specific Job Duties:Understand customers’ risk use cases, challenges, and define plans to achieve success criteriaDevelop, implement, and evolve risk strategies with clientsPerform risk pattern analysis, build Executive dashboards, monitor fraud detection performanceLead technical discussion for fraud deep dives that prepare for detection modules’ success Guide and work with our ML modeling team to monitor and enhance the detection quality by performing UML and/or SML model performance metricsLead the implementation of detection logic and rules to enhance detection qualityAct as technical liaison between clients and internal teamsRegularly present customers' key success metrics to internal and external stakeholders.This role may require travel to visit customers to strengthen client relationships, onsite workshops, and quarterly business reviews
Requirements:2+ years of fraud/risk management and analytics experience in banking, fintech, or payment industries, where machine learning models and rules are utilized to prevent scam-based and account takeover-based banking transaction fraud, card payment fraud, etc.Excellent data analytics skills using tools like SQL, Python, RExperience in building dashboards through Tableau or MetabaseProven track record performing deep dive analysis on fraud patterns and decision strategiesExcellent communication and presentation skillsStrong time management skills and a sense of project ownershipPrior Banking/FinTech experience is a plusPrior fraud consulting experience is a plusB.A./B.S. degree in a technical or analytical discipline