Lead Data Scientist – Underwriting AI

The  Lead Data Scientist – Underwriting  is a senior leadership role responsible for driving  regional and BU-level AI, ML, and Generative AI solutions  to transform underwriting decision-making across Asia. This role combines  deep technical expertise, people leadership, and strong business partnership  to deliver scalable, compliant, and value-generating underwriting capabilities. You will  lead and grow a team of data scientists  focused on underwriting AI, while also acting as a hands-on technical leader shaping advanced predictive, prescriptive, and generative solutions.

The role partners closely with underwriting, risk, technology, and regional stakeholders, and supports the  Chief AI Officer  in delivering the Asia AI Value agenda and building world-class, revenue-generating analytics. Position Responsibilities: Technical & Business Leadership Lead the  design, development, and deployment  of AI, ML, and  Generative AI  solutions supporting underwriting use cases, including: Underwriting summarization and document intelligence Risk assessment and decision support Straight-through processing (STP) and workflow automation Data-driven underwriting rules optimization Translate complex underwriting and business problems into  scalable analytical and GenAI solutions  with measurable business outcomes.

Drive adoption of AI solutions by embedding them into  end-to-end underwriting workflows , ensuring usability, governance, and regulatory compliance. Partner with reinsurers, vendors, and external technology providers to co-develop or integrate advanced underwriting solutions. Lead, coach, and develop a high-performing team of data scientists  focused on underwriting AI and analytics. Generative AI & Advanced Analytics Lead the application of  AI and especially GenAI  in underwriting, including: Large Language Models (LLMs) for text-heavy underwriting data (e.g., medical reports, financial statements, applications) Prompt engineering, retrieval-augmented generation (RAG), and evaluation frameworks Model orchestration, guardrails, and human-in-the-loop designs Ensure AI solutions are  responsible, explainable, auditable, and compliant  with internal AI governance and regulatory expectations.

Establish standards for  model lifecycle management , performance monitoring, and value tracking for AI and GenAI solutions. Strategy, Governance & Stakeholder Management Contribute to  AI and data strategy , focusing on data optimization, monetization, and external data enrichment. Collaborate with global and regional teams to define  common modeling standards, governance, and reusable AI assets , aligned with AI CoE guidelines.

Maintain a regional/global  model repository  to track models in use, performance, and risks. Act as a trusted advisor to underwriting and business leaders, influencing prioritization and investment decisions. Proactively identify risks, data challenges, and stakeholder conflicts, and drive alignment and mitigation plans. Required Qualifications: 10+ years of experience in  AI, machine learning, or advanced analytics , preferably within financial services.

Strong underwriting, insurance, or reinsurance domain knowledge (highly preferred). Proven experience leading complex analytics initiatives in  highly regulated environments . Technical & GenAI Skills Strong hands-on expertise in  ML/statistical modeling , segmentation, and underwriting or claims analytics. Advanced proficiency in  Python, SQL , and common data science toolchains (R, SAS a plus).

Practical experience with  Generative AI , including: Large Language Models (LLMs) Prompt engineering and RAG architectures Model evaluation, bias mitigation, and explainability AI governance and responsible AI practices Experience with  cloud and big data platforms  (e.g., Azure, Spark, Databricks, Hadoop). Strong understanding of deploying models into production, including monitoring and lifecycle management.

Leadership & Communication Demonstrat