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Security Researcher
Not specifiedFull-timeSecurity ResearchCybersecurityEnterprise Software
Research fraud and abuse patterns and develop practical detection and prevention capabilities for a SaaS product. The role focuses on browser or mobile technologies, real-world telemetry, and collaboration with data science, product, and engineering teams.
Responsibilities
- Research emerging fraud and abuse techniques, including account takeover, bots, phishing, automation, device spoofing, and related attack vectors.
- Identify and validate new data collection opportunities, security signals, behavioral patterns, and device, browser, and mobile indicators.
- Analyze telemetry, customer-provided labels, behavioral and device signals, network indicators, and attack patterns to find detection opportunities.
- Reproduce attacker techniques, generate telemetry, identify detection gaps, and translate findings into detection logic.
- Design and tune detection and prevention mechanisms, considering coverage, explainability, false positives, customer impact, and production stability.
- Build research infrastructure, analysis workflows, and internal tools using Python.
- Create data features for machine-learning models, detection logic, rules, dashboards, and customer insights.
- Work with model-training pipelines, evaluate model behavior, and assess whether new features improve detection quality.
- Collaborate with data science, engineering, and product teams from research hypothesis through implementation, monitoring, and production feedback.
- Track developments in fraud, attacker tooling, browser and mobile abuse, malware, phishing, and underground ecosystems; communicate findings and recommendations to stakeholders.
Requirements
- At least 3 years of experience in security research, fraud research, detection engineering, threat research, mobile security, browser security, or a similar technical role.
- Bachelor’s degree in Computer Science, Cybersecurity, Data Science, or a related field, or equivalent hands-on experience.
- Hands-on experience with desktop or mobile browsers and web security, or native Android/iOS applications and mobile security.
- Strong understanding of web and mobile technologies, APIs, application behavior, and modern attack techniques.
- Strong Python skills and experience building research tools, automation, data analysis workflows, detection prototypes, or feature pipelines.
- Experience analyzing real-world data, investigating anomalies, validating hypotheses, and drawing conclusions from incomplete information.
- Familiarity with machine-learning training and validation concepts, including feature quality, overfitting, precision and recall, and model evaluation.
- Ability to produce structured, reliable data features and design explainable, scalable defenses.
- Strong problem-solving and communication skills, with the ability to work independently and collaborate across technical teams.
Nice to have
- Experience researching both browser-based and mobile-native environments.
- Familiarity with fraud types, bots, automation, browser internals, mobile application security, fingerprinting, malware, phishing, or fraud ecosystems.
- Experience measuring detection quality, tuning rules, evaluating models and features, or monitoring production systems.
- Experience with notebooks, pandas, SQL, data visualization, or machine-learning pipelines.