Director Data Science -Enterprise SaaS- $110K - $120K

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, DevOps, 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 NumPy, Pandas, Scikit-learn, NetworkX, and TensorFlow/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/OpenSearch 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