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.

Responsibilities:

• 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
  
  

Advantage:

• 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