Note: The job is a remote job and is open to candidates in USA. ExtraHop develops Network Detection and Response solutions that help organizations detect, investigate, and respond to cyber threats in real time. The Senior Data Scientist will analyze large-scale network telemetry, develop and evaluate machine learning and artificial intelligence methods, and improve threat detection capabilities. The role also involves collaborating with threat researchers and software engineers, providing technical leadership, and translating research into reliable product capabilities.
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
Skills
- 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
- 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
Benefits
- Health, Dental, and Vision Benefits
- Flexible PTO, Sick Time Prorated Based on Date of Hire, and All Federal Holidays (US Only) + 3 Days of Paid Volunteer Time
- Non-Commissioned Positions may be eligible to participate in the Annual Discretionary Bonus Plan
- FSA and Dependent Care Accounts + EAP, where applicable
- Educational Reimbursement
- 401k with Employer Match or Pension where applicable
- Pet Insurance (US Only)
- Parental Leave (US Only)
- Hybrid and Remote Work Model
Company Overview
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