Senior Data Scientist

Join ExtraHop, a leading cybersecurity company, as a Senior Data Scientist. In this role, you will analyze large-scale network telemetry to identify behavioral patterns and anomalies associated with malicious activity. You will design, develop, and refine machine learning and AI-based methods that support ExtraHop's products. Additionally, you will conduct exploratory data analysis, feature engineering, model development, and error analysis using complex, high-volume datasets. You will also provide technical leadership and mentorship to other data scientists and communicate findings and recommendations to technical stakeholders and product leaders.

• Analyser des télémetries réseau à grande échelle pour identifier des modèles de comportement et des anomalies.
• Concevoir, développer et affiner des méthodes basées sur l'apprentissage automatique et l'IA pour soutenir les produits d'ExtraHop.
• Établir des métriques et des stratégies de surveillance pour mesurer la performance des modèles et des détecteurs au fil du temps.
• Experience collaborating with software engineers to integrate data science methods into production systems
• 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
• Strong problem-solving skills, intellectual curiosity, and a track record of independently owning complex technical work
• Bachelor’s degree in Data Science, Computer Science, Mathematics, or another quantitative discipline, or equivalent practical experience
• Strong foundation in statistics, experimental design, machine learning, and model evaluation
• 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
• Excellent written and verbal communication skills, including the ability to explain technical findings, uncertainty, and tradeoffs to varied audiences
• Experience working with incomplete, noisy, or highly imbalanced data; performing detailed error analysis; and identifying the causes of false positives and false negatives
• 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