Senior Data Scientist

extrahopnetworks; United States; On-site; USD 130,000 - 190,000; Full time

Job summary

ExtraHop is seeking a Senior Data Scientist to advance machine learning and AI capabilities powering our Network Detection and Response products. You will analyze large-scale network telemetry, build robust models for threat detection, and improve detection accuracy in real time.

You'll collaborate with data scientists, threat researchers, and software engineers to translate research into reliable product features, write production-grade code, and mentor junior team members in rigorous

Qualifications

• Bachelor's degree in Data Science, CS, Mathematics, or equivalent practical experience.
• 7+ years of professional data science experience with ML models and evaluations.
• Strong foundation in statistics, experimental design, ML, and model evaluation.
• Experience with incomplete/imbalanced data; error analysis and false positives/negatives.
• Proficiency in Python and SQL; write maintainable, tested production-grade code.
• Experience collaborating with software engineers to deploy data science methods.

Responsibilities

• Analyze large-scale network telemetry to identify patterns and signals of malicious activity.
• Design, develop, and refine ML/AI methods supporting ExtraHop's products.
• Develop evaluation methods for cybersecurity data (imbalanced datasets, rare events).
• Conduct EDA, feature engineering, model development, and error analysis on large datasets.
• Research emerging ML techniques and assess applicability to products.
• Establish metrics and monitoring for model/detector performance over time.
• Communicate findings and tradeoffs clearly to stakeholders and leaders.
• Provide technical leadership and code reviews within the team.
• Document methodologies, experiments, and results; write clean production-grade code.

Skills

Python

SQL

Statistics

Machine learning

Experimentation

Data analysis

Communication

Education

Bachelor's in Data Science/CS/Math

Tools

TensorFlow

PyTorch

Git

Job description

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.

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 and prototypes into reliable product capabilities.

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.
• 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.
• 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.
• 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, behavioral 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