Position Summary
ExtraHop is seeking a Senior Engineering Manager to lead the Applied Machine Learning team responsible for behavioral detections within the ExtraHop Network Detection and Response (NDR) platform.
This team develops machine learning systems that analyze large-scale network telemetry and surface meaningful behavioral signals for Security Operations Center (SOC) analysts. The work sits at the intersection of applied machine learning, cybersecurity, and high-volume time-series data.
This role owns the applied machine learning strategy for behavioral detection within the product. You will lead a team responsible for designing, evaluating, and operationalizing models that identify anomalous or suspicious patterns in complex network activity. The position combines technical leadership, scientific rigor, and product influence to ensure machine learning capabilities translate directly into actionable security insights.
Key Responsibilities
- Lead and grow a multidisciplinary team of data scientists and software engineers building production machine learning models and supporting systems for behavioral detection.
- Drive the research, development, evaluation, and operational monitoring of models that analyze large-scale network telemetry, including time-series and behavioral anomaly detection.
- Establish high standards for experimental rigor across the team, including statistically sound experimentation, clear evaluation methodologies, and disciplined model validation.
- Own the technical direction for production ML systems supporting behavioral detections, including experimentation frameworks, model lifecycle management, data pipelines, and monitoring.
- Collaborate closely with Product Management and Security Research to translate machine learning capabilities into practical detection signals that improve SOC analyst workflows.
- Influence the product roadmap by identifying opportunities where applied machine learning can materially improve detection quality and analyst productivity.
- Mentor senior data scientists and engineers while fostering a culture of scientific rigor, intellectual curiosity, and technical ownership.
- Represent the machine learning function in cross-organizational discussions and communicate technical strategy and outcomes to senior leadership.
- Stay current with advances in machine learning research and engineering practices and guide the team in adopting techniques that meaningfully improve detection performance.
Required Qualifications
- Bachelor’s degree or equivalent experience in Computer Science, Statistics, Machine Learning, or a related quantitative field; advanced degree preferred.
- 5+ years experience leading applied machine learning or machine learning engineering teams delivering production systems.
- Strong background in machine learning, statistics, or a related quantitative discipline.
- Experience guiding experimental design, model evaluation strategies, and statistically rigorous decision making.
- Experience building or operating production ML systems, including model lifecycle management, data pipelines, and monitoring.
- Experience working with large-scale telemetry, time-series data, or behavioral modeling problems.
- Demonstrated ability to partner with product and domain experts to translate machine learning capabilities into user-facing value.
- Strong technical judgment and the ability to guide architecture and modeling decisions.
- Experience mentoring senior individual contributors and building high-performing ML teams.
- Exceptional communication skills; able to translate model performance, technical tradeoffs, and data science concepts for product, executive, and cross-functional audiences.
- Consistent, reliable, and accountable in attendance and execution.
Preferred Qualifications
- Experience with network security, NDR, or related security domains; familiarity with tools and frameworks commonly used in threat detection.
- Experience building ML systems on AWS cloud infrastructure, including data pipelines and model deployment at scale.
- Familiarity with compliance requirements such as FedRAMP or NIST SP 800-53 and their implications for data science workloads.
- Experience with containerization technologies (Docker, Kubernetes) for ML workload deployment.
- AWS certification such as AWS Certified Solutions Architect or Machine Learning Specialty.
- Understanding of threat detection, intrusion prevention, and incident response strategies.