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Quantitative Research Intern – AI Investment Research

Job in Genf, Geneva, Switzerland
Listing for: Lombard Odier Group
Apprenticeship/Internship position
Listed on 2026-10-09
Job specializations:
  • Finance & Banking
    Portfolio & Asset Management, Data Scientist, Financial Analyst
Salary/Wage Range or Industry Benchmark: 268000 - 469000 CHF Yearly CHF 268000.00 469000.00 YEAR
Job Description & How to Apply Below
Position: Quantitative Research Intern – AI for Investment Research
Location: Genf

A career at Lombard Odier means working for a renowned global wealth and asset manager, with a strong focus on sustainable investing. An innovative bank of choice for private and institutional clients, our independently owned Firm is one of the best‑capitalised banking groups in the world, managing close to CHF 300 billion and operating from over 25 offices across 4 continents.

With a history spanning over 230 years,
Lombard Odier is an investment house providing a comprehensive offering of discretionary and advisory portfolio management, wealth services and custody. We also offer asset management services and investment strategies through Lombard Odier Investment Managers and provide advanced banking technology to other financial institutions.

“Rethink Everything” is our philosophy – it is at the heart of everything we do. We have grown stronger through more than 40 financial crises by rethinking the world around us to provide a fresh investment perspective for our clients.

Lombard Odier Investment Managers (LOIM) is the asset management business of the Lombard Odier Group. In order to strengthen our Investment team, we are looking for a:

Quantitative Research Intern – AI for Investment Research

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 Lang Chain, Lang Graph, Llama Index, or similar tools.
  • Experience building RAG systems, AI agents, or evaluation frameworks.
  • Familiarity with cloud‑based AI platforms and APIs.
  • Prior…
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