Senior Data Scientist

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. The ability to write production code in Python is required for this role. 

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.
• Provide technical leadership and mentorship to other data scientists through code reviews, design discussions, technical strategy, and contributions to the team’s analytical and engineering standards.
• Document methodologies, experiments, model behavior, and evaluation results.
• Write clean, maintainable, and well-tested production-quality code.

Required Qualifications

• Bachelor’s degree in Data Science, Computer Science, Mathematics, or another quantitative discipline, or equivalent practical experience.
• Strong proficiency in Python and SQL, experience with common data science and machine learning libraries, and the ability to write maintainable, tested, and reviewable code.
• 7+ years of professional experience in data science, including developing and evaluating machine learning models for real-world use cases, defining meaningful success metrics, and conducting rigorous offline and online evaluations.
• Strong foundation in statistics, experimental design, machine learning, and model evaluation.
• Experience working with incomplete, noisy, or highly imbalanced data; performing detailed error analysis; and identifying the causes of false positives and false negatives.
• Experience collaborating with software engineers to integrate data science methods into production systems.
• Strong problem-solving skills, intellectual curiosity, and a track record of independently owning complex technical work.
• Excellent written and verbal communication skills, including the ability to explain technical findings, uncertainty, and tradeoffs to varied audiences.

Preferred Qualifications

• Master’s degree or Ph.D. in Data Science, Computer Science, Mathematics, or another quantitative discipline.
• Experience applying data science or machine learning to cybersecurity, fraud detection, abuse detection, anomaly detection, or another adversarial domain.
• Familiarity with Network Detection and Response, network protocols, threat detection, or incident investigation.
• Experience with time-series analysis, anomaly detection, actuarial modeling, clustering, graph analytics, or unsupervised and semi-supervised learning.
• Experience evaluating models in domains where positive examples are rare, labels are incomplete, and the underlying behavior changes over time.
• Experience with generative AI, large language models, agentic systems, or other emerging AI techniques.
• Familiarity with cloud platforms such as AWS or GCP.

Compensation & Benefits: The salary range for this role is $165,000 – $180,000 per year, plus eligibility for an annual performance-based discretionary bonus and stock option grants. We offer a comprehensive benefits package including medical, dental, and vision coverage (effective ay 1), a 401(k) with employer match, flexible PTO, parental leave, and more.