GenAI Data Scientist, Singapore

Job Responsibilities Advanced Analytics & AI Development Apply Machine Learning (ML), Deep Learning (DL), and Generative AI (GenAI) techniques to solve real-world insurance challenges across underwriting, pricing, claims, and customer experience. Execute the full modeling lifecycle: data integration, model selection, validation, deployment, and client engagement. Generative AI Innovation Explore and implement GenAI use cases such as underwriting automation, claims optimization, and agentic workflows for summarization, inference, and information retrieval. Combine traditional predictive analytics with GenAI to enhance efficiency and unlock new business value. Project Management & Collaboration Lead and participate in analytics projects across Asia-Pacific, Middle East, and Africa. Work closely with actuaries, underwriters, IT teams, and global analytics centers to deliver impactful solutions. Present analytics findings and solutions to internal and external stakeholders. Continuous Improvement & Knowledge Sharing Support business units with advanced research methods and provide specialized know-how in AI and analytics. Develop and implement solutions to improve operational efficiency and business performance. Conduct training and share best practices within the analytics community.
Job Requirements Master's or Ph.D. in Data Science, AI, Statistics, Applied Mathematics, Computer Science, Engineering, or related field (preferred). Minimum 2 years of industry experience in data science, AI, or ML. Hands-on experience in Python (front-end, back-end, API integrations); full-stack development and JavaScript visualization are advantages. Strong theoretical knowledge of GenAI, ML, and DL. Familiarity with RESTful APIs, microservices, and LLM applications. Experience with GenAI workflows (summarization, inference, information retrieval) is a plus. Demonstrable projects on Kaggle, GitHub, or analytics blogs are an advantage. Insurance/reinsurance experience, especially in claims automation, is desirable. Proficiency in Python and related libraries for ML/DL. Understanding of SDLC and model deployment processes. Knowledge of big data technologies and cloud platforms is a plus. Strong stakeholder management and ability to explain technical concepts to non-technical audiences. Excellent documentation and presentation skills. Innovative mindset, ability to work under tight timelines, and willingness to travel within Asia.