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Senior Data Scientist
On-siteFull-timeData ScienceCybersecurity
Lead end-to-end Data Science initiatives for security policy management systems, developing production-grade machine learning, LLM, and AI agent capabilities. Own problem framing, evaluation, deployment, monitoring, and optimization while providing technical leadership across multidisciplinary teams
Responsibilities
- Lead Data Science initiatives from problem framing through validation, CI/CD-based production deployment, monitoring, and operational optimization
- Develop predictive modeling, anomaly detection, classification, behavioral analysis, and other advanced machine learning capabilities
- Build intelligent capabilities for security AI agents using machine learning, LLMs, statistical methods, and domain-specific algorithms
- Adapt and fine-tune LLMs for security use cases
- Define evaluation methodologies for ML and agentic AI systems, covering decision quality, reliability, robustness, uncertainty, and failure modes
- Partner with Product, Engineering, and Security teams to deliver measurable 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
- Deep understanding of machine learning theory, statistical reasoning, and practical model behavior
- Strong Python expertise and experience with modern Data Science libraries including NumPy, Pandas, Scikit-learn, PyTorch, or TensorFlow
- Strong understanding of LLM architectures, adaptation, and fine-tuning
- Strong understanding of AI agent architectures, including tool use, context management, memory, planning, reasoning, and multi-step workflows
- Strong analytical rigor, structured problem-solving ability, and effective collaboration in multidisciplinary product teams
Nice to have
- Experience developing or deploying AI agents or multi-step reasoning systems
- Experience with local or on-premise AI under constrained compute, memory, latency, or security requirements
- Experience with small language models, quantization, distillation, or efficient inference
- 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