Data Science, Cobalt

• Graduate student (MS/PhD) in a quantitative field
• Strong foundation in at least one of: applied math, probability, statistics, and modeling
• Proficiency in Python
• Experience with agentic LLM-based AI systems

Desirable

• We are pushing the boundaries of agentic AI in the offensive security space, leveraging over 10 years of real-world offensive security data to train and evaluate our models
• Our work sits at the intersection of applied mathematics, statistical modeling, and autonomous AI systems, focused on high-stakes, adversarial environments
• We are seeking a graduate-level Data Science Intern with a strong foundation in applied mathematics or statistics and hands-on experience with agentic AI systems
• In this role, you will contribute to the development of autonomous agents, improving their ability to reason, adapt, and exploit findings in both test and real-world environments
• You will also develop models that capture complex system interactions and evaluate how agents perform under evolving platform conditions
• Build and improve autonomous agents, focusing on reasoning, planning, and exploitation workflows
• Enhance agent performance in both simulated and real-world environments
• Analyze outputs from agents to identify system weaknesses and emergent behaviors
• Collaborate with engineering and security teams to integrate models and agents into real-world offensive security workflows
• Apply techniques from:
• Causal inference
• Probabilistic modeling
• Optimization
• Design and run experiments to validate model and agent assumptions
• Work with large-scale datasets to calibrate and test models
• Iterate on agent behavior based on empirical results and failure analysis
• Communicate findings clearly to technical stakeholders