we are embedding AI-driven capabilities across its insurance pricing and rating platform. To accelerate this, we are looking for a student with a strong analytical and mathematical background who can serve as a bridge between the theoretical foundations of our algorithms and their implementation in production code. This role offers a unique opportunity to bridge the gap between the theoretical foundations of our company's algorithms and their implementation in production code. You will create AI-powered skills and knowledge bases that enable developers to understand what specific functions do, how they relate to the underlying mathematics, and how they connect to the broader algorithmic pipeline. Youll work on cutting-edge projects involving LLMs, agentic capabilities, and structured knowledge management - solving complex, real-world challenges while making a tangible impact on the evolution of our AI products.
?What youll do: Review and interpret theoretical and mathematical descriptions of algorithms used in the company platform (e.g. GLMs, gradient boosting, optimization routines, rating engines) and map them to corresponding functions, classes, and modules in the codebase. Author structured AI skills (prompt-based documentation packages) that allow an AI assistant to explain to a Developer what a given function does, its mathematical basis, inputs, outputs, and role in the end-to-end process. Create and maintain a structured knowledge base linking mathematical specifications to code artefacts, ensuring it is accurate, testable, and maintainable as algorithms evolve. Optimize, evaluate, and refine AI solutions to enhance performance, accuracy, and efficiency of generated explanations. Expand training and testing data for AI skills, leveraging both hand-crafted and automated examples. Identify gaps where code behavior diverges from or extends the documented theory, and address challenges in AI architecture scalability and reliability. Support the wider AI enablement program by contributing to prompt engineering, testing, and iteration of AI-powered Developer tools.
Position Intro:
we are the premier provider of mission-critical, cloud-based intelligent decisioning across pricing, rating, underwriting, and product personalization. These fully-integrated solutions provide ultra-fast ROI and are designed to transform how global insurers and banks are run by unlocking value across all facets of the business. our company has been innovating for insurers and banks since 2001 with customers in over 35 countries across six continents and offices in the Americas, Europe, Asia Pacific, and Israel.
Requirements: Youll do it using (Requirements): Currently pursuing a degree in actuarial science, mathematics, statistics, Computer Science, data science, or a closely related quantitative discipline, with at least 3 semesters left to graduation. Strong grounding in probability, statistics, and mathematical modelling. Ability to read and interpret formal algorithm descriptions, research papers, and technical specifications. Accomplished relevant courses (at least one) such as Machine Learning, Deep Learning, NLP, or Information Retrieval, with a GPA of 90 and above. Comfortable reading code in Python, R, JAVA, or C ++ and tracing logic through a large codebase. Familiarity with version control (Git) and navigating large repositories. Familiarity with AI tools, frameworks, ML concepts, and prompt engineering fundamentals. Excellent written English with the ability to explain complex mathematical concepts in plain, structured language.
Advantages: Familiarity with core actuarial or insurance modelling techniques (e.g. GLMs, survival models). Completed courses in Prompt Engineering or LLMs. Hands-on experience with RAG pipelines, embedding models, vector databases, and retrieval optimization. Exposure to Machine Learning frameworks (scikit-learn, XGBoost, TensorFlow) or optimization libraries.
This position is open to all candidates.