Threat Analyst, Machine Learning Operations & Detection Efficacy (Remote)

As a global leader in cybersecurity, CrowdStrike protects the people, processes and technologies that drive modern organizations. Since 2011, our mission hasn’t changed — we’re here to stop breaches, and we’ve redefined modern security with the world’s most advanced AI-native platform. We work on large scale distributed systems, processing almost 3 trillion events per day and this traffic is growing daily.

Our customers span all industries, and they count on CrowdStrike to keep their businesses running, their communities safe and their lives moving forward. We're proud to work for a mission-driven company leveraging AI to transform the way we work. CrowdStrikers drive their careers through flexibility and autonomy while also being expected to contribute to a culture of responsible AI adoption, experimentation, and innovation.

We use an AI-first mindset as a force multiplier to proactively and continuously accelerate execution, build expertise, uncover insights, and solve complex problems. We’re always looking to add talented CrowdStrikers to the team who have limitless passion, a relentless focus on innovation and a fanatical commitment to our customers, our community and each other. Ready to join a mission that matters? The future of cybersecurity starts with you.

About the Role:

Our CrowdStrike Data Science Machine Learning Operations and Response Team is looking for a Threat Analyst who is both customer- and team-focused. This role will include both response and pro-active aspects. This role will be perfect for anyone who has experience involving detections and/or reverse engineering. The primary responsibility of this role will be to analyze malware and detections by investigating individual customer detection tickets.

This team is focused on improving detection capability and efficiency through analysis of malware or other threat detections impacting our customer base. Experience with detections of potentially malicious behavior by machine learning models is a plus.

Bring your passion for helping internal partners resolve questions about detections of potentially malicious activity by machine learning and our detection capability. Our goal for the team is to both help internal teams respond to customer-inquiries about threat detection and to provide information about the effects of our detections into the Data Science organization - including detection efficacy and managing false positive detections.

This role will work most closely with internal teams such as Technical Account Managers, Falcon Complete, Data Scientists and the Malware Research Center in Data Science.

What You'll Do:

• Analyze detection data including customer reports to determine which aspects of the machine learning models can be improved
• Perform tasks to enable better-management of false positive detections
• Analyze binary files to determine their legitimacy
• Review current product detections to ensure they are performing to the company standard
• Address internal questions and concerns regarding customer threat detections

What You'll Need:

• Exposure and understanding of different types and functionality of malware
• Experience with reverse engineering malware, or malware operations
• Knowledge of programming and scripting languages, in particular Python
• Fundamental understanding of attributes of binary files such as imports/exports and packers
• Ability to demonstrate practical knowledge of research/collection skills and analytical methods
• General understanding of threat/risk management and threat/risk assessment
• Familiarity with various operating systems
• Ability to break down complex problems into workable components

Proven experience utilizing AI technologies to enhance decision-making, streamline workflows and processes, improve efficiency and drive business outcomes.

Bonus Points:

• Experience in a security operations center or similar environment responding to incidents
• A thorough understanding of Windows OS internals and the Windows API
• Knowledge of MacOS and/or Linux
• Familiarity with tools used in targeted and criminal cyber-intrusions
• A background in exploit and vulnerability analysis
• Knowledge of a variety of programming languages including C, C++, Java, and assembly

Experience with threat detections by machine learning .