Quantitative Researcher - 2027 Graduates

At Graviton, we don't believe the best trading strategies come from following established playbooks. They come from asking better questions. Why does a pattern exist? What is driving it? Is it real or just noise? Can it be explained, modelled and turned into an edge?

As a Quantitative Researcher, you'll work on those questions every day. You'll investigate market behaviour, develop quantitative models, build and test hypotheses and turn insights into trading strategies, working alongside researchers and technologists in an environment where an idea on a whiteboard can reach live trading quickly.

This isn't a role where the answers are in a textbook. The problems are open-ended, the data is enormous, and finding the answers is the job.

Ideal candidates are rigorous thinkers who genuinely enjoy mathematics, programming and figuring out things that aren't obvious, based in our Singapore, London, Amsterdam or US offices.

What You'll Work On

You'll work across quantitative research and systematic trading, taking ideas through the full lifecycle: question, hypothesis, experiment, model, validation, strategy, production. Depending on your team and research interests, your work would include:

• Discovering predictive patterns and sources of alpha from billions of market events
• Formulating hypotheses about market behaviour and designing experiments to test them
• Applying probability, statistics, optimization and machine learning to build predictive models and quantitative signals for systematic trading
• Designing robust backtests and statistical tests to distinguish genuine signals from noise
• Working with large and complex datasets to uncover patterns that aren't immediately visible
• Investigating market microstructure and how markets behave at different timescales
• Evaluating strategy performance, identifying weaknesses and continuously refining models
• Developing research infrastructure and AI-powered tools and agents that make experimentation faster and analysis deeper
• Working closely with technologists to translate research ideas into efficient, production-ready systems
• Exploring new techniques in machine learning, statistics and quantitative finance to uncover new sources of edge

No two research problems are the same. Good research finds patterns. Great research understands why they exist.

What Success Looks Like

Successful researchers at Graviton discover new sources of alpha and turn them into robust trading strategies, build models that hold up under real market conditions and not only in backtests, develop a deep understanding of market behaviour, create research frameworks and tools that make the broader team more effective, work with technologists to take ideas from research into production, and communicate complex quantitative ideas clearly and rigorously. Your impact isn't measured by how many models you build. It's measured by the quality of the ideas you discover and what those ideas enable.

Who We're Looking For

We're looking for exceptional problem solvers who genuinely enjoy mathematics, programming and figuring out things that aren't obvious.

• Degree in a quantitative discipline such as mathematics, physics, computer science, statistics, operations research or machine learning
• No active backlogs
• Strong programming skills in Python and/or C++
• Solid grounding in probability, statistics, linear algebra and optimization, with experience in machine learning, statistical modelling or data analysis, whether through coursework, research or personal projects
• Comfort working with large, complex datasets, and strong analytical thinking
• A passion for applying quantitative thinking and rigorous research to solve open-ended problems in markets
• Excellent communication skills, with the ability to explain complex ideas clearly

Prior exposure to quantitative research, algorithmic trading, market microstructure, competitive mathematics or machine learning is valuable, but we care more about how you think than whether you've worked in finance before.

Our Culture

More than specific tools or techniques, we care how you think. We look for people who are curious enough to keep asking why, analytical enough to separate signal from noise, persistent enough to pursue difficult problems, intellectually honest enough to abandon an idea when the evidence says so, and humble enough to keep learning.

At Graviton, researchers sit alongside technologists on specialized trading teams that operate with significant autonomy. You'll have meaningful ownership of research problems from day one: forming your own hypotheses, challenging existing approaches and following the evidence wherever it leads, with the infrastructure and datasets to test ideas at scale, competitive compensation, and a culture where the best idea wins regardless of who proposed it.

Markets are among the most complex systems humans have ever created, and they never stop changing. Neither should our thinking. Every day, someone asks a better question and finds an answer nobody had. That someone could be you.

This quantitative research role applies mathematics, programming, and data analysis to discover market patterns and develop systematic trading strategies. Researchers test hypotheses, build and validate models, and work with technologists to move effective ideas into production.

Role at a glance

Skills mentioned

What you will likely work on

• Form hypotheses about market behavior and design experiments to test them.
• Analyze large market datasets to identify predictive patterns and potential sources of alpha.
• Use statistical, mathematical, and machine-learning methods to build models and trading signals.
• Create backtests and statistical tests to assess whether signals are robust or driven by noise.
• Review strategy results and refine models to address weaknesses and changing market conditions.
• Develop research tools and infrastructure, including AI-powered tools and agents, to support experimentation.
• Partner with technologists to turn research ideas into efficient production systems.
• Communicate quantitative findings clearly and rigorously.

Good fit signals

• A degree in a quantitative discipline such as mathematics, physics, computer science, statistics, operations research, or machine learning.
• Strong programming ability in Python and/or C++, alongside a solid foundation in probability, statistics, linear algebra, and optimization.
• Experience with machine learning, statistical modeling, or data analysis through coursework, research, or personal projects.
• Comfort analyzing large, complex datasets and working through open-ended problems with analytical rigor.
• Prior exposure to quantitative research, algorithmic trading, market microstructure, competitive mathematics, or machine learning is valuable, but not required.