Formulate and test hypotheses
Work with event-level market data to model price dynamics, build signals or evaluate strategies measured against real outcomes.
Quantitative Research Internship
Intro
Our global research teams turn vast, fast-moving market data into predictive signals and models that strengthen pricing and systematic strategies. As a Quantitative Research Intern, you’ll work alongside experienced researchers on a scoped project drawn from active priorities. You’ll build models, test ideas in code and show whether your research can improve how we trade.
Research at Optiver
Research at Optiver
Research at Optiver
What you'll do
Forecast financial market behavior
Undertake and apply research, machine learning, and mathematical modeling to predict price movement.
Design for production constraints
Build models and analysis that account for noise, regime shifts, latency and the gap between backtest and live performance.
Beyond academia: Research in the trading industry
Research at a trading firm is different from research in academia. The problems are less defined, the feedback loops are shorter, and the work is tied directly to real-world outcomes. At Optiver, research doesn’t happen in isolation. Researchers work side by side with traders and engineers, shaping ideas together and seeing how they behave in production. What we look for in candidates reflects that reality: working with noisy data, testing ideas in live systems, and iterating quickly alongside traders and engineers.
Our models operate in live, competitive markets where outcomes are uncertain and conditions change quickly. You should be comfortable reasoning about noisy data, imperfect signals, and risk—not just optimizing models on clean datasets.
Projects built for real impact
Intern projects are drawn from real problems the business is actively working on. These aren’t hypothetical exercises or case studies. Each project is scoped in advance by the Quant Research leadership team, based on current priorities and where interns can contribute meaningfully during their internship. While projects vary year over year, they consistently reflect problems our quant research team is actively working on.
Parameter automation
Use historical data to model and automate trading control parameters that are currently set manually.
Network optimisation
Improve the performance and reliability of messaging between our systems and an exchange.
Leader-lagger strategies
Analyse how price signals in one product can inform pricing and trading decisions in related instruments.
ETF relationships
Study pricing relationships between highly liquid ETFs (e.g. QQQ) and newer or less liquid counterparts (e.g. SQQQ), and develop models to derive fair value.
Correlated futures trading
Identify patterns across related futures contracts, evaluate historical data, and build models to predict price movements.
PhD Quant Focus
Early Careers
This program is designed to show how your academic studies apply to the practical challenges of quantitative research and applied machine learning in global financial markets. Work alongside Optiver’s quantitative researchers, traders, and engineers in a series of hands-on sessions that mirror the types of problems they solve and how they approach them in practice.
Learn more
Intern voices
“From this internship, I significantly improved my understanding of the industry through learning about click trading, features, strategy and back testing.”
“All of the research mentors were exceptional. They all had different ways of thinking, so whenever I interacted with them, I would pick up new ideas which I found really valuable”
“From this internship, I significantly improved my understanding of the industry through learning about click trading, features, strategy and back testing”
From Computer Science to Quant Research
Hear from past interns
With a background in software engineering, Lucy explores a new career path in quantitative research. Follow her journey as she discovers the fast-paced, challenging world of trading through an internship in our Amsterdam office.
Read more
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