Graduate Quantitative Researcher (2027 Start)
Optiverus · London
What this role requires
2 requirements, read out of the advert rather than guessed from the job title:
Also mentioned, not required: C, C++, Basic, Java, deep learning, probability, calculus, linear algebra, English. Worth having, but their absence is not what gets a CV filtered out.
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Job description
If you enjoy using mathematics and code to investigate difficult questions, you could make a direct contribution to Optiver’s success in global markets. As a Graduate Quantitative Researcher, you’ll work across the full research process, from framing the question and choosing the data to building, testing and deploying models. Machine Learning is central to the work, whether you’re extracting signals, forecasting prices or modelling relationships. Our data, compute and production infrastructure let you test ideas quickly, understand where they fail and decide what to investigate next.
Our researchers believe in academic discourse and invite fellow researchers, traders and engineers to challenge each hypothesis. Different perspectives help improve the question, the model and how it performs in production. Through constant testing, analysis and refinement, you’ll help our quantitative models adapt as markets evolve.
WHAT YOU’LL DO
Your onboarding
You’ll participate in Global Optiver Academy , a structured onboarding programme for graduates. It gives new joiners a shared foundation in how Optiver trades, builds systems, and manages risk before they move into their teams. As part of your onboarding, you'll gain exposure to the AI tools and technologies that support research and development across the business.
Your responsibilities
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Depending on the team you join, you'll own the research end to end. That could include:
Building models for pricing, forecasting and signal discovery, choosing the statistical, mathematical or Machine Learning methods that best fit the problem.
Working across petabytes of market, order-flow and unstructured data to find signals that improve strategies as conditions change.
Monitoring models in production and using live behavior to refine your assumptions and decide what to investigate next.
Using deep learning and AI-enabled workflows to shorten the loop from hypothesis to evidence.
Working directly with researchers, traders and engineers to shape problems and understand how system constraints affect performance.
WHO YOU ARE
We're looking for aspiring Quantitative Researchers who are versatile and creative in innovating and suggesting new solutions. In return, we'll give you the freedom to pursue your ideas and implement them right into our production systems.
In terms of skills and qualifications, we're looking for:
An academic degree in Engineering, Physics, Maths, Econometrics, Computer science or equivalent, with outstanding academic achievements
Programming experience in any language (preferably Python, but C, C++, Basic, JAVA, etc. are also a plus)
Ability to apply concepts of probability, calculus and linear algebra
Competitive attitude and eagerness to constantly improve
Ability to learn quickly
Excellent verbal and written English language skills
WHAT YOU’LL GET
You’ll join a culture of collaboration and excellence, where you’ll be surrounded by curious thinkers and creative problem solvers. Motivated by a passion for continuous improvement, you’ll thrive in a supportive, high-performing environment alongside talent colleagues, working collaboratively to tackle the toughest problems in the financial markets.
In addition, you’ll receive:
A performance-based bonus structure, enabling all of our employees to benefit from our global profit pool
The opportunity to work alongside best-in-class professionals from over 50 countries
25 paid vacation days in your first year, increasing to 30 from your second year onwards
Training opportunities, discounts on health benefits, and fully paid first-class commuting expenses
Extensive office perks, including breakfast, lunch and dinner, world-class barista coffee, bi-weekly chair massages, Friday afternoon drinks, a bicycle to explore Amsterdam and much more
Training and continuous learning opportunities, including access to conferences and tech events
Competitive relocation packages and visa sponsorship where necessary for expats
HOW TO APPLY
Are you interested in furthering your career on one of the most dynamic and exciting trading floors in Europe?
Apply directly via the form below for the position of Graduate Quantitative Researcher. Please provide us with a CV in English. Unfortunately we cannot accept applications via email for data protection reasons.
If your application is selected to proceed to the next round, you will be required to complete online assessments. By submitting your application you are confirming that you will be able to take the assessment within 7 days.
Kindly note that we do not require any assistance from third-parties including agencies in the recruitment of this role. If you have any questions feel free to contact our Recruitment team via our recruitment inquiry form.
Diversity statement
Optiver is committed to diversity and inclusion .
We encourage applications from candidates from any and all backgrounds, and we welcome requests for reasonable adjustments during the process to ensure that you can best demonstrate your abilities. Please let us know if you would like to request any reasonable adjustments by contacting the Recruitment team via the contact form , selecting "Reasonable Adjustments" as the subject of your inquiry
For answers to some of our most frequently asked questions, refer to our recruitment FAQ .
For applicants based in India, our entry route is via the placement office internship hiring season (July/August).
*Optiver has a global application re-apply policy for our intern and graduate roles. If you have completed an online assessment or interviewed for a quantitative graduate or internship role at any Optiver location in the past 8 months, please note that you are not yet eligible to reapply. We welcome you to re-apply after the 8-month cool off period.
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