Data Scientist

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.