Lead end-to-end data science initiatives to design, build, deploy, monitor, and optimize security-focused AI systems. Develop ML models, LLM fine-tuning, AI agents, and partner with product, engineering, and security teams while mentoring others.
Description
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 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.
Key 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
- Design and implement security-focused AI agents and reasoning systems
- Develop advanced ML capabilities, including predictive modeling, anomaly detection, and classification
- Adapt and fine-tune LLM technologies for domain-specific security use cases
- Partner with Product, Engineering, and Security teams to deliver measurable business impact
- Provide technical leadership and mentorship across multidisciplinary Data Science initiatives
- 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 and practical model behavior
- Strong expertise in Python and the modern Data Science ecosystem (NumPy, Pandas, Scikit-learn, PyTorch / TensorFlow, etc.)
- Solid understanding of LLM architectures and fine-tuning methodologies
- Excellent interpersonal skills and proven ability to work within multidisciplinary product teams
- Strong analytical rigor and structured problem-solving capability
Advantage
- Experience with ML observability, model monitoring, or drift detection.
- Experience with graph technologies, such as Neo4j.
- Experience with Generative AI, RAG, GraphRAG, semantic search, vector databases, or domain-specific LLM fine-tuning / adaptation.
- Experience designing and deploying AI agents or multi-step reasoning systems in on-premise environments.
- Background in network security, firewall policies, or compliance analytics.
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