Director data science -cybersecurity / identity security platform- $110k - $130k

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, Gen AI, 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, Network X, 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 Ph D 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 (Network X or similar) SQL, Mongo DB, or equivalent Distributed data processing (Spark or similar) MLOps, model monitoring, and model lifecycle management Experience applying LLMs/Gen AI to cybersecurity or enterprise data analytics

Interested or know someone who fits? Write to rukmini@— happy to share more details