We are seeking a highly motivated Quantitative Research Intern to help develop AI-driven research tools and investment insights across equity and fixed income investment strategies, including both long-only and long-short portfolios. The successful candidate will work closely with quantitative researchers, portfolio managers, and investment analysts to explore how Large Language Models (LLMs) and modern machine learning techniques can enhance the investment research process. This internship provides an opportunity to work on cutting-edge applications of AI in asset management, ranging from company and issuer analysis to the creation of novel investment signals derived from structured and unstructured data. The role is research-oriented, hands-on, and ideal for candidates with a strong quantitative background who are excited about applying frontier AI technologies to real-world investment problems.
YOUR ROLE
As a Quantitative Research Intern, you will:
- Develop AI- and LLM-based tools to support investment research and decision-making.
- Build systems that extract, summarize, and analyse information from corporate filings, earnings calls, credit research, central bank communications, news, and other unstructured datasets.
- Design and evaluate retrieval-augmented generation (RAG) and agentic AI workflows for investment research applications.
- Explore the use of foundation models to improve information discovery, monitoring, and knowledge management across investment teams.
- Apply statistical and machine learning techniques to structured financial, macroeconomic, and market datasets.
- Assist in developing novel research signals and indicators derived from textual, alternative, and market data sources.
- Prototype tools that integrate internal databases, research repositories, and external data providers.
- Evaluate the robustness, reliability, and investment relevance of AI-generated insights.
- Present findings through research reports and presentations to investment professionals.
YOUR PROFILE
- Knowledge of equity and/or fixed income investing.
- Familiarity with portfolio construction, risk management, and systematic investment processes.
- Experience working with financial datasets such as company fundamentals, earnings transcripts, credit metrics, bond market data, analyst estimates, or macroeconomic data.
- Experience with LLM frameworks such as LangChain, LangGraph, LlamaIndex, or similar tools.
- Experience building RAG systems, AI agents, or evaluation frameworks.
- Familiarity with cloud-based AI platforms and APIs.
- Prior research experience in machine learning, NLP, or applied AI.
Example Projects
- Developing an AI-powered research assistant capable of analysing company filings, earnings transcripts, credit research reports, and internal investment documents.
- Building systems that identify and summarize material changes in corporate fundamentals, credit quality, and macroeconomic conditions.
- Creating novel textual and alternative-data signals for equity and fixed income investment strategies.
- Designing multi-agent workflows that automate parts of the investment research process across long-only and long-short portfolios.
- Evaluating the effectiveness of AI-generated insights relative to traditional quantitative signals and research methods.
- Building natural language interfaces that allow investment professionals to query internal databases, market data, and research repositories.
