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12 часов назад

Security Researcher (Cybersecurity)

Формат работы
hybrid
Тип работы
fulltime
Грейд
middle
Английский
b2
Страна
Israel
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

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TL;DR

Security Researcher (Cybersecurity): Researching, designing, and improving advanced fraud and abuse detection capabilities for a SaaS identity and anti-fraud platform with an accent on browser and mobile-native application security. Focus on reproducing attacker techniques, analyzing telemetry, and developing production-grade detection mechanisms to prevent account takeovers and bot attacks.

Location: Hybrid in Tel Aviv-Yafo, Israel

Company

hirify.global provides a cutting-edge platform that fuses customer identity management and anti-fraud solutions for large consumer-facing businesses.

What you will do

  • Research emerging fraud and abuse techniques including account takeovers, bots, phishing, and device spoofing.
  • Identify and validate new security signals and behavioral patterns across desktop/mobile browsers and native applications.
  • Analyze real-world telemetry and reproduce attacker techniques to identify and fill detection gaps.
  • Design, validate, and tune detection and prevention mechanisms for production stability and accuracy.
  • Build research infrastructure, analysis workflows, and internal tools using Python.
  • Collaborate with data science, engineering, and product teams to translate research hypotheses into production features.

Requirements

  • At least 3 years of experience in security research, fraud detection, or threat research.
  • Bachelor’s degree in Computer Science, Cybersecurity, Data Science, or equivalent hands-on experience.
  • Strong expertise in either browser security (APIs, automation, fingerprinting) or mobile security (Android/iOS behavior, emulators).
  • Proficiency in Python for building research tools and feature engineering pipelines.
  • Experience analyzing complex real-world data and investigating anomalies to draw practical conclusions.
  • Familiarity with machine learning concepts such as train/test splits, precision/recall, and model evaluation.

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