Search jobs

About Armilla AI

Armilla AI is a cutting-edge startup based in Toronto, Ontario, Canada, operating at the intersection of artificial intelligence and insurance. We're not just talking about AI risk; we're actively building the solutions to manage, underwrite, and insure it. Our dynamic team is passionate about pioneering the future of AI risk management through the development of sophisticated AI-powered tools and programmatic evaluation platforms.

The Role

We're on the hunt for a truly unique individual – a "unicorn" who seamlessly blends deep actuarial expertise with robust Artificial Intelligence and Machine Learning capabilities. As our Senior Actuarial Data Scientist, AI Risk, you'll be pivotal in developing and implementing our AI risk assessment and pricing frameworks. This isn't just a role; it's an opportunity to shape an emerging industry.

Role Responsibilities

In this exciting role, you'll

• Assess and evaluate AI models to identify insurable risks, potential vulnerabilities, and failure modes.
• Work hand-in-hand with our underwriters to structure comprehensive and innovative insurance coverage for various AI systems and applications.
• Develop sophisticated rating tables and pricing models that accurately quantify and price complex AI-related risks.
• Leverage your strong programming skills in Python to build and implement analytical tools, prototypes, and automated processes.
• Dive into the world of Large Language Models (LLMs) and other advanced AI techniques to enhance our risk assessment, prediction, and mitigation strategies.
• Conduct in-depth quantitative analysis and research, staying ahead of emerging AI risks and industry trends.
• Translate complex technical concepts into clear, actionable insights for both technical and non-technical stakeholders.

What We're Looking For

We're seeking a candidate who brings

• Strong knowledge of the insurance industry and core actuarial principles, including hands-on experience with risk modeling, pricing, and reserving. Any actuarial designations will be an asset.
• A strong background in data science, with a proven track record in statistical modeling, machine learning, and predictive analytics.
• Expertise in Python is essential, along with practical experience in relevant libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch.
• Familiarity with Large Language Models (LLMs) and their potential applications in risk assessment is a significant asset.
• The ability to thrive both independently and collaboratively in a fast-paced, startup environment.