A mature and successful identity technology company is looking for a Security Researcher to join its research team and help uncover, understand, and prevent sophisticated fraud and abuse.
The platform operates at large scale and analyzes real-time signals across browsers, mobile devices, applications, networks, and user behavior. This role focuses on turning deep technical research into production-ready detection capabilities that improve fraud prevention and customer protection.
You will investigate how attackers operate, identify valuable new signals, validate detection ideas, and work closely with Data Science, Engineering, and Product to bring those ideas into the platform.
Key Responsibilities
• Research emerging fraud and abuse techniques across web, mobile, device, browser, and network environments
• Analyze production data, attack patterns, customer labels, and behavioral signals to identify new detection opportunities
• Reproduce attacker techniques in controlled environments and uncover detection gaps
• Translate research findings into detection logic, data features, rules, and product capabilities
• Build research tools, automation, and analysis workflows using Python
• Evaluate and improve detection quality, including precision, recall, false positives, and production stability
• Collaborate with Data Science, Engineering, and Product from research hypothesis through production deployment
• Communicate findings, risks, and recommendations to technical and business stakeholders
Required Experience
• At least 3 years of experience in Security Research, Fraud Research, Detection Engineering, Threat Research, Browser Security, Mobile Security, or a similar hands-on role
• Strong expertise in at least one of the following areas:
– Browser technologies, browser APIs, automation, fingerprinting, web signals, or client-side security
– Android or iOS applications, mobile operating systems, device signals, emulators, instrumentation, automation, or mobile security
• Strong understanding of modern web and mobile technologies, APIs, application behavior, and attacker techniques
• Advanced Python skills and experience building research tools, automation, analytical workflows, detection prototypes, or feature pipelines
• Experience working with noisy, incomplete, and real-world datasets
• Ability to identify anomalies, test hypotheses, validate findings, and draw actionable conclusions
• Familiarity with machine-learning fundamentals, including train and validation datasets, feature quality, precision, recall, false positives, false negatives, overfitting, and data leakage
• Experience producing stable, structured, and model-ready data features
• Ability to combine offensive thinking with practical, scalable, and explainable defensive solutions
• Strong ownership, persistence, independence, and problem-solving skills
• Excellent communication and collaboration abilities
• Experience working closely with Engineering, Product, and Data Science teams