[![Image]() Meet Jobicy Copilot — free AI autofill for job applications + remote job alerts ›](#) [All remote jobs](https://jobicy.com/jobs.md)Open role[![ExtraHop logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/4800f505-221.jpg)](https://jobicy.com/company/extrahop.md)Remote opportunity at[ExtraHop](https://jobicy.com/company/extrahop.md) # Senior Data Scientist Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/extrahop.md)Share11 Sep 2026Published36Listing views1Application actions11 Oct 2026Apply before Opportunity details ## About this role. AI SummaryExtraHop is hiring a Senior Data Scientist to strengthen machine learning and AI capabilities for its Network Detection and Response platform. The role analyzes large-scale network telemetry to identify malicious behavior, develops and evaluates detection models, and establishes robust performance monitoring. It requires deep expertise in statistics, experimentation, Python, SQL, and machine-learning evaluation under incomplete labels, rare events, and class imbalance. The successful candidate will partner with threat researchers and engineers, contribute production-quality code, and provide technical leadership to other data scientists. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a senior applied ML role in an adversarial cybersecurity setting, where labels are incomplete, positive events are rare, and attacker behavior evolves over time. The candidate must independently drive ambiguous research-to-production work while maintaining rigorous evaluation standards and mentoring peers. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianMarket rate$172,500US market range$155k–$205k0$226k AI insightThe disclosed base salary range is $165,000-$180,000 USD yearly, with a midpoint of $172,500. This is competitive for a US-based senior data scientist specializing in applied machine learning and cybersecurity; a broader US market estimate is approximately $155,000-$205,000 annually, excluding bonus, equity, and benefits. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Applied Data Science](https://jobicy.com/jobs?search_keywords=Applied%20Data%20Science.md)[Cybersecurity Analytics](https://jobicy.com/jobs?search_keywords=Cybersecurity%20Analytics.md)[Anomaly Detection](https://jobicy.com/jobs?search_keywords=Anomaly%20Detection.md)[Network Telemetry](https://jobicy.com/jobs?search_keywords=Network%20Telemetry.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Statistical Modeling](https://jobicy.com/jobs?search_keywords=Statistical%20Modeling.md)[Model Evaluation](https://jobicy.com/jobs?search_keywords=Model%20Evaluation.md)[Threat Detection](https://jobicy.com/jobs?search_keywords=Threat%20Detection.md) Sample interview questionsHow would you evaluate a threat-detection model when positive labels are rare and incomplete?I would combine available confirmed labels with precision-focused review samples, stratified analysis, and proxy metrics tied to analyst workload and detection quality. I would measure precision-recall behavior rather than rely on accuracy, analyze false positives and false negatives, and monitor performance over time for label drift and behavior changes. Describe an approach for detecting anomalous behavior in high-volume network telemetry. I would first define the relevant entity and time window, such as host, user, protocol, or connection flow. I would engineer behavioral, temporal, and peer-group features, establish robust baselines, and test supervised, semi-supervised, or unsupervised methods based on label quality. Detection output would be calibrated and evaluated with operationally meaningful thresholds and investigation feedback. How do you move a data science method from research into a reliable product feature? I start with a clear success metric, reproducible data and evaluation pipelines, and explicit assumptions. I work with engineers on data contracts, latency and scaling constraints, test coverage, deployment design, and monitoring. After launch, I track model quality, drift, system health, and user feedback, then iterate based on measurable outcomes. Tell us how you would investigate a sudden increase in false positives from a detector. I would segment alerts by customer environment, time period, feature values, software version, and relevant network context to isolate where the shift occurred. I would compare recent input distributions and labeling practices with historical baselines, review representative cases with threat researchers, and determine whether the cause is data drift, a pipeline change, threshold calibration, or genuine behavioral change. I would then implement a targeted correction and validate it through offline and controlled production evaluation. How do you communicate uncertainty and model tradeoffs to technical and product stakeholders? I translate uncertainty into the decision at hand by clearly stating what the model detects reliably, where it is likely to fail, and the expected operational cost of errors. I use concise visualizations and scenario-based metrics such as precision at a given alert volume, while documenting assumptions, data limitations, and recommended next steps. This helps stakeholders choose thresholds and roadmap priorities with realistic expectations. 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. If this sounds like a place you’d like to spend the next chapter of your career, we’d love to hear from you. 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. 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, 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. The salary range for this role is $165,000 – $180,000 + bonus + benefits ABOUT EXTRAHOP ExtraHop is reinventing Network Detection and Response (NDR) to offer enterprises unparalleled visibility, context, and control against emerging threats. The platform integrates NDR with Network Performance Management (NPM), Intrusion Detection Systems (IDS), and forensics, providing a single, comprehensive solution. By decrypting and analyzing complete packet-level data at wire speed and leveraging cloud-scale machine learning, ExtraHop empowers Security Operations Centers (SOCs) to detect, investigate, and remediate modern cyber risks in real time across their entire hybrid infrastructure, including data center, cloud, and SASE environments. This comprehensive approach and market innovation have earned ExtraHop unique recognition as the only NDR vendor acknowledged as a leader by all major analyst firms, including the 2025 Gartner® Magic Quadrant for Network Detection and Response™, the 2025 Forrester® Wave for Network Analysis and Visibility, the 2024 IDC® Marketscape for NDR, and the 2025 Gigamon® Radar Report for Network Detection and Response. Since 2007, ExtraHop has consistently helped organizations worldwide extract in-depth network telemetry and contextual insights, affirming its commitment to protecting and empowering the connected enterprise. OUR VALUES Our culture is rooted in our five Values. These set the expectations for how we work individually and collectively as a team. Lead with Purpose: We are driven to deliver results that create a positive impact for our customers, partners, and colleagues. Act with Integrity: We operate with transparency, authenticity, and always in the best interest of the company. Find a Way: We are resourceful, tackle hard problems with a sense of urgency and ownership, and do what it takes to get the job done. Innovate: We listen to customers, partners, and the market, and respectfully push boundaries and challenge the status quo. Share Success: We run together, we win together. We value diverse perspectives, hold space for all voices, and achieve the best results as a team. BENEFITS Employees’ wellbeing is top of mind for the ExtraHop team. Employees and their families will have the option to participate in the following 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 Our people are our most important competitive advantage, leading the charge against cyber criminals. Join the fight today! To learn more, visit [our website](https://www.extrahop.com/) or follow us on [LinkedIn](https://www.linkedin.com/company/extrahop-networks/). Create a Job Alert Interested in building your career at ExtraHop? Get future opportunities sent straight to your email. 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