Develop ML-driven pricing models to generate underwriting decisions autonomously, increasing capacity from 100 to 1000+ submissions per underwriter per month.
Design models to forecast risk and optimize pricing strategies using structured and unstructured data.
Implement AI models that extract insights from loss run reports, reducing manual work and accelerating turnaround times.
Use NLP and deep learning to process structured and unstructured insurance documents, improving decision-making efficiency.
Develop and deploy computer vision models to analyze property imagery for key underwriting features such as roof material, condition, and structure.
Build a chatbot leveraging LLMs to assist underwriters with document analysis and risk assessment.
Requirements
3+ years of experience in software engineering with a focus on machine learning, AI, or data science.
Proven experience building and deploying LLM applications in production.
Strong background in time series forecasting, NLP, and predictive modeling.