AVP/VP, Data Scientist – Corporate Services Transformation

About the role

GIC, one of the world's largest sovereign wealth funds, is hiring an AVP/VP Data Scientist within its Investment Insights Group. The role sits in the Data Science & Innovation team, GIC's center of excellence for AI, where the Corporate Services Transformation sub-group uses AI platforms to transform corporate functions such as Finance, HR, Cybersecurity and Legal & Compliance. You will build AI solutions that run in production and demonstrate their business value.

Key responsibilities

• Lead the design and delivery of LLM and agentic AI solutions for Finance, HR, Cybersecurity and Legal & Compliance
• Own productionisation end to end: architecture, deployment, monitoring, security and cost
• Partner with senior stakeholders to shape the AI roadmap, prioritise use cases and drive adoption
• Design tools and MCP servers connecting AI agents to enterprise systems such as SAP, and set standards for agent harness design and evaluation
• Lead data pipeline design on AWS and Databricks, working with the IIG platform team
• Define the ROI framework for solutions and report realised benefits to senior management
• Mentor Associates and AVPs and raise the team's technical bar

Required profile

• Degree in a quantitative discipline (Computer Science, Engineering, Mathematics, Physics, Statistics, Finance or similar)
• At least 7 years of relevant experience, including 4 years of hands-on delivery and experience mentoring others
• Strong track record of producing LLM-based solutions at scale in an industry setting
• Demonstrated stakeholder management with senior business leaders, including managing expectations and driving change

Required skills

• AI frameworks such as LangChain, LangGraph and Claude, with deep experience in agentic architectures and agent harness design
• Building tools and MCP servers or equivalent integrations to enterprise systems, with security and auditability
• Software engineering and architecture skills (testing, CI/CD, cloud-native deployment)
• Data science skills: metrics, evaluation methodology, model mechanics and LLM fine-tuning
• Building and leading delivery of data pipelines on AWS, Databricks or similar
• Ability to measure ROI and communicate it to senior, non-technical audiences
• Nice to have: knowledge graphs and graph-based retrieval (e.g. GraphRAG), enterprise application integration (e.g. SAP), data governance and responsible AI in a regulated environment