Data Scientist

At ExtraHop, we’re on a mission to protect and empower the connected enterprise. We reveal what is happening in the very infrastructure that sustains businesses, lives, and communities, and ensure the integrity of networks, data, systems, and processes. Organizations rely on ExtraHop to provide visibility into the cyber threats, vulnerabilities, and network performance issues that evade their existing security and IT tools. With this insight, organizations can investigate smarter, stop threats faster, and keep operations running.

Our mission is fueled by a profound social and moral responsibility to be the best at what we do, ensuring a secure world where everyone can thrive. If this sounds like a place you’d like to spend the next chapter of your career, we’d love to hear from you. 

Position Summary

ExtraHop is at the forefront of cybersecurity innovation, delivering Network Detection and Response (NDR) solutions that help organizations detect, investigate, and respond to cyber threats in real time.

Join ExtraHop as a Senior Data Scientist and help advance the machine learning and artificial intelligence capabilities that power our products. You will analyze large-scale network telemetry, develop and evaluate methods for identifying malicious behavior, and improve the accuracy and efficacy of ExtraHop’s threat detection capabilities.

This is an applied data science role for someone who enjoys solving ambiguous, high-impact problems using statistical analysis, experimentation, and machine learning. You will work closely with other data scientists, threat researchers, and software engineers to translate research into reliable product capabilities, and play a key role in contributing to the improvement of the RevealX platform.

Key Responsibilities

• Analyze large-scale network telemetry to identify behavioral patterns, anomalies, and signals associated with malicious activity.
• Design, develop, and refine machine learning and AI-based methods that support ExtraHop’s products.
• Develop evaluation methods appropriate for cybersecurity data, including highly imbalanced datasets, incomplete labels, rare events, and changing attacker and network behavior.
• Conduct exploratory data analysis, feature engineering, model development, and error analysis using complex, high-volume datasets.
• Research emerging machine learning and AI techniques, relevant cybersecurity developments, and assess their applicability to ExtraHop’s products.
• Establish metrics and monitoring strategies for measuring model and detector performance over time.
• Communicate findings, limitations, tradeoffs, and recommendations clearly to technical stakeholders and product leaders.