Data Scientist

Description

• Design, develop, and evaluate agentic AI systems capable of autonomous or semi-autonomous reasoning, planning, and action in cybersecurity use cases.
• Analyze large, complex datasets to identify patterns and trends that could indicate potential security threats. 
• Collaborate closely with cybersecurity researchers, threat analysts, and architects to translate real-world attack scenarios into AI-driven detection, investigation, and response capabilities.
• Design and develop innovative prompts and instruction sets to enhance the conversational capabilities of our language models.
• Develop and optimize ML- and LLM-powered security workflows, including multi-step reasoning, tool use, memory, and feedback loops.
• Stay current with advances in cybersecurity, adversarial ML, agentic AI architectures, and applied LLM research, and contribute to internal best practices.
• Partner with software and data engineers to deploy AI agents and ML models into production-grade, secure, and scalable systems.

• Bachelor’s degree in computer science, Engineering, or a related field. A master's or Ph.D. is a plus.
• Strong foundation in machine learning and deep learning, including supervised, unsupervised, and anomaly-detection techniques.
• Extensive experience applying data science, machine learning, and agentic AI systems to cybersecurity domains, including threat detection and response.
• Proven ability to deliver reliable, high-quality AI systems in fast-paced, evolving environments.
• Proficiency in Python. Experience with PySpark, Databricks, or large-scale data processing frameworks is a plus.
• Experience working with LLMs (open-source and commercial) in production or research settings, including model selection, evaluation, and safe deployment.
• Familiarity with vector databases, embeddings, and retrieval-augmented generation (RAG) for security or knowledge-intensive workflows.
• Understanding of LLM and AI security considerations, such as adversarial inputs, model exploitation, data privacy, and governance.
• Experience with cloud-based ML platforms and modern MLOps practices is a plus.
• Strong analytical and problem-solving skills, with the ability to reason under uncertainty and adversarial conditions.
• Excellent communication and collaboration skills, with the ability to work effectively across security, engineering, and research teams.