Manager, Economic Services - Data Science

Responsibilities

• Create econometric analysis, machine learning and deep learning models on economic, regulatory and policy issues of significance to a range of clients in the public and private sector
• Build LLM-powered applications and contribute to Agentic AI system Design for complex tasks
• Design, build, and productionize Retrieval-Augmented Generation (RAG) systems, including document ingestion, embedding pipelines, vector search, and LLM orchestration
• Contribute to and maintain a repository of internal tools and client-facing solutions, showcasing best practices in software development and model deployment
• Develop and document prototypes and production-level code for a variety of AI and machine learning applications; develop proof of concept, prototype, and minimum viable products
• Craft rigorous, insightful narratives on the issues under analysis and translate these into informative professional reports, dashboards, and visualizations that are accessible to technical and non-technical audience
• Act with integrity, professionalism, and personal responsibility to uphold KPMG's respectful and courteous work environment

Qualifications

• Minimum of five years of recent experience with advisory related projects within a quantitative analysis firm, consulting firm, or background in a scientific laboratory
• PhD in the areas of Computer Science, Mathematics, Mathematical Statistics, Electrical Engineering is required
• A portfolio of projects (such as via GitHub, personal website, or other platforms) that demonstrates expertise in building and deploying AI/ML models and NLP solutions
• Experience building data science or LLM-based products, with a demonstrable track record of your personal contributions to the codebase
• Demonstrated experience in the development, application and interpretation of complex economic and statistical modeling frameworks using Python or R
• Strong written and verbal communication skills, excellent research skills, and the ability to work well both autonomously and in a team environment