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Job Description
About the role
This role combines deep technical expertise with leadership responsibility, driving multidisciplinary Data Science initiatives end-to-end within complex security policy management systems.
The role drives the development of innovative, production-grade AI capabilities, including the intelligence behind security AI agents and advanced machine learning models built on complex security data.
Original thinking, deep technical rigor, intellectual agility, and exceptional problem-solving are essential.
What you'll do
Lead end-to-end Data Science initiatives from problem framing through validation, CI/CD-based production deployment, monitoring, and ongoing operational optimization of AI systems
Develop advanced ML capabilities, including predictive modeling, anomaly detection, classification, and behavioral analysis
Develop the intelligent capabilities behind security AI agents, combining machine learning, LLMs, statistical methods, and domain-specific algorithms, with a strong understanding of how agents use these capabilities within multi-step workflows
Adapt and fine-tune LLM technologies for domain-specific security use cases
Define and implement rigorous evaluation methodologies for ML and agentic AI systems, including decision quality, reliability, robustness, uncertainty, and failure modes
Partner with Product, Engineering, and Security teams to deliver measurable business impact
Provide technical leadership and mentorship across multidisciplinary Data Science initiatives
Requirements
M.Sc. in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative discipline
At least 7 years of hands-on Data Science experience, delivering end-to-end solutions into production environments
Deep understanding of machine learning theory, statistical reasoning, and practical model behavior
Strong expertise in Python and the modern Data Science ecosystem (NumPy, Pandas, Scikit-learn, PyTorch / TensorFlow, etc.)
Strong understanding of LLM architectures, adaptation and fine-tuning methodologies
Strong understanding of AI agent architectures and concepts, including tool use, context management, memory, planning/reasoning, and multi-step workflows
Strong analytical rigor and structured problem-solving capability
Excellent interpersonal skills and proven ability to work within multidisciplinary product teams
Nice to have
Experience developing or deploying AI agents or multi-step reasoning systems
Experience with local/on-premise AI systems, particularly under constrained compute, memory, latency, or security requirements
Experience with small language models (SLMs), model quantization, distillation, efficient inference, or other techniques for running AI models locally
Experience with Generative AI, RAG, GraphRAG, semantic search, vector databases, or domain-specific LLM adaptation
Experience with ML/AI observability, model monitoring, or drift detection
Experience with graph technologies, such as Neo4j
Background in network security, firewall policies, or compliance analytics