About Kanan
Kanan is building AI software to help manufacturers and financial institutions understand supply chains, assess risk, and make informed decisions. We are bringing together a founding team across AI research, engineering, and data science.
About the role
As Chief Scientific Officer, you will lead the scientific development of Kanan’s AI capabilities. You will work directly with the CEO to turn complex business problems into a focused research agenda, and with engineers and data scientists to develop methods that can be tested and used in practice.
This is a hands-on role spanning research, experimentation, and technical guidance. Your work will connect advances in AI with practical questions about how systems represent knowledge, retain context, reason under uncertainty, and learn from evidence. You will help the team choose methods on the strength of their results and understand where further research is needed. You will also help establish a shared research practice so that experiments, findings, and technical decisions remain understandable as the team grows.
Responsibilities
- Set the company’s AI research direction, identify the most valuable scientific questions, and translate them into practical development priorities.
- Develop approaches to reasoning, learning, memory, and knowledge representation that can work across different types of business information.
- Guide the use of language models, knowledge graphs, and agent-based systems, choosing methods that fit the problem and available evidence.
- Establish evaluation methods for accuracy, reliability, and uncertainty, and use experimental results to guide research and product decisions.
- Work with engineers and data scientists to turn research into working software, review technical approaches, and document important assumptions and limitations.
Required qualifications
- Strong research experience in AI, machine learning, computational neuroscience, cognitive science, or a related discipline, with relevant computational work.
- Understanding of learning, cognition, and decision-making, and the ability to connect these ideas to the design of AI systems.
- Evidence of developing and testing research ideas through code, experiments, or applied work, with a clear account of what the results demonstrate.
- Ability to explain scientific choices clearly, collaborate across disciplines, and guide a small technical team through open-ended research problems.
Preferred experience
- Experience with knowledge representation, probabilistic reasoning, or neuroscience-informed approaches to AI.
- Experience translating research into commercial applications and balancing scientific goals with real-world data and product constraints.