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 225 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 Technology team, we are looking for a:
Technical Quant Developer - Intern
You will join a technology-driven financial organization where quantitative research and advanced software development come together to deliver data-driven insights. Our developers, portfolio managers, and analysts work closely to build and optimize tools and libraries for our proprietary quant platform, which supports trading, risk management, and portfolio construction.
As a Technical Quant Developer Intern, you will be part of a collaborative team, gaining hands-on experience developing infrastructure for quantitative research and financial modeling using Java and Python. You’ll work on real-world projects alongside experienced professionals, building your technical and analytical skills in a dynamic financial environment.
YOUR ROLE* Develop, test, and maintain quant libraries, analytical tools, and financial models in Java and Python.
Support the design and implementation of data ingestion, transformation, and storage solutions for financial datasets
Collaborate with quantitative researchers, analysts, and developers to improve model performance, scalability, and usability
Write clean, efficient, and well-documented code aligned with best practices
YOUR PROFILE* Master’s degree (ongoing or completed) in Computer Science, Mathematics, Finance or a related field
Strong programming skills in Java and Python (academic, project-based, or self-taught).
Solid understanding of data structures, algorithms, and object-oriented programming.
Familiarity with development tools and practices (Git, testing, debugging).
Strong problem-solving skills and interest in quantitative methods.
Prior exposure to software development, data science, or finance (a plus, not required).
Experience with numerical computing libraries (e.g., NumPy, pandas, SciPy, scikit-learn).
Knowledge of databases (SQL/NoSQL) and data pipelines.
Understanding of probability, statistics, or financial instruments.
Familiarity with distributed systems, cloud platforms, or parallel computing.
Our Maison’s DNA is defined by five core values. Excellence drives us to be the best at what we do, while Innovation fuels our progress. Respect underpins every interaction, and Integrity shapes our actions. Together, we are One Team, united in serving our clients with unwavering dedication.
As a responsible and supportive employer, we promote a diverse and inclusive work environment for our employees and candidates. Diversity, Equity and Inclusion are woven into the fabric of our Maison’s DNA, and we strive to ensure that our employees can fulfill both their personal and professional aspirations by encouraging internal mobility and individual upskilling programs. We firmly believe that building Diverse Teams contributes to our successes and to deliver on this, we actively embed Diversity, Equity and Inclusion in our business strategy.
It is an exciting time to join our Teams. All applications will be handled in the strictest confidence.