Job Description & How to Apply Below
We're building the Data Science and ML capabilities that power an enterprise cybersecurity platform — analyzing billions of events, flows, logs, and identity signals to help customers detect risk before it becomes a breach. If you combine deep technical expertise with strong engineering leadership, this is your next big challenge.
This is a hands-on leadership role in a fast-moving startup environment, balancing technical depth, product thinking, execution, and team building.
What You'll Own
Define and drive the technical vision, roadmap, and execution for Data Science, ML, AI, and advanced analytics capabilities
Build large-scale analytics solutions capable of processing billions of events, network flows, logs, identities, and security signals
Own Data Science/ML components end-to-end — from problem definition through production deployment, monitoring, and optimization
Apply supervised/unsupervised learning, anomaly detection, clustering, classification, and graph analytics to solve complex cybersecurity problems
Drive data preparation, feature engineering, model development, and experimentation across large, diverse security datasets
Build analytical and ML-based capabilities for threat detection, behavioral analysis, identity/access analytics, and security posture
Make key architectural and technical decisions, establishing engineering best practices for the function
Partner closely with Engineering, QA, UI, Dev Ops, IT/Ops, Product Management, and senior leadership to take solutions from concept to production
Build, mentor, and grow a strong Data Science/ML team with a high technical bar
Evaluate and adopt advances in AI/ML, GenAI, and graph analytics where they create real product value
What You Bring
15+ years of hands-on experience in Data Science, ML, AI, Analytics, or a closely related field, with a strong record of building production-grade solutions
Strong hands-on expertise in ML/AI techniques, algorithms, and statistical methods
Deep understanding of supervised and unsupervised learning — classification, clustering, anomaly detection, dimensionality reduction
Strong programming experience in Python, with frameworks like Num Py, Pandas, Scikit-learn, NetworkX, and Tensor Flow/Keras
Experience with large-scale datasets and distributed/cloud environments
Strong grasp of software engineering principles — architecture, scalability, reliability, performance, testing, CI/CD, production operations
Demonstrated ability to take ambiguous problems from definition to production solution
Bachelor's, Master's, or PhD in Computer Science, Data Science, Mathematics, Statistics, Engineering, or equivalent practical experience
Good to Have
Experience in cybersecurity, network security, identity security, or enterprise security analytics
Experience analyzing network traffic, flows, security events, audit logs, identity data, or telemetry
Event/log analytics platforms (ELK/Open Search or equivalent)
Graph analytics and graph-based ML (NetworkX or similar)
SQL, MongoDB, or equivalent
Distributed data processing (Spark or similar)
MLOps, model monitoring, and model lifecycle management
Experience applying LLMs/GenAI to cybersecurity or enterprise data analytics
Interested or know someone who fits? Write to — happy to share more details
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