Design and implement Agentic AI solutions that automate business workflows, improve decision-making, and enhance customer and employee experiences.
Apply LLMs and generative AI to process and interpret unstructured data such as contracts, underwriting notes, claims, medical records, and customer interactions.
Develop autonomous agents and reasoning systems that integrate with Guardian’s platforms to deliver measurable business outcomes.
Collaborate with data engineers and AIOps teams to ensure models are scalable, robust, and production-ready.
Translate research in agentic AI and reinforcement learning into practical applications for underwriting, claims automation, customer servicing, and risk assessment.
Work closely with product owners, engineers, and business stakeholders to define use cases, design solutions, and measure impact.
Contribute to the Data Science Lab by building reusable components and frameworks for developing and deploying agentic AI solutions.
Adhere to AI and LLM governance, documentation, testing, and other best practices in partnership with key stakeholders.

PhD with 0–1 years of experience, Master’s degree with 2+ years, or Bachelor’s degree with 4+ years in Statistics, Computer Science, Engineering, Applied Mathematics, or related field.
Experience in insurance industry (Underwriting Experience is Preferred)
2+ years of hands-on experience in AI/ML modeling and development.
Solid understanding of probability, statistics, and machine learning fundamentals.
Strong programming skills in Python and familiarity with frameworks like PyTorch, TensorFlow, and LangGraph.
Experience with LLMs, generative AI, and multi-step reasoning systems.
Excellent problem-solving and analytical skills with attention to detail.
Strong communication skills and ability to collaborate effectively with product and engineering teams.
Working knowledge of core software engineering concepts (version control with Git/GitHub, testing, logging, ...).
Working knowledge of a variety of machine learning techniques (clustering, decision tree, bagging/boosting artificial neural networks, etc.) and their real-world advantages/drawbacks.

Data Scientist

Key skills