Maven is a market-leading proprietary trading firm deploying its own capital across discretionary, systematic and market-making strategies. Combining expertise in trading, technology and research, we develop advanced pricing and execution capabilities to improve liquidity across global listed derivatives markets.

The role

As a Senior Quantitative Researcher, you will lead research that directly influences our options market-making performance. You will turn market observations, trader intuition and data into systematic, measurable and scalable improvements across pricing, prediction, execution and risk-taking.

Working as an equal partner with Trading and Technology, you will proactively identify opportunities, develop and test quantitative hypotheses, and take ownership of ideas from initial research through to measurable impact in production.

Alongside delivering improvements to individual strategies, you will help make successful research reusable across desks, products and markets, ensuring that each advance strengthens our platform and informs future research.

What you’ll do

Lead research across option pricing, volatility modelling, alpha generation and algorithm design for low-latency, exchange-traded options strategies.
Translate complex trading questions and practitioner intuition into testable hypotheses, quantitative models and optimisation problems.
Apply mathematics, statistics, machine learning and optimisation where appropriate, using large datasets to develop and rigorously validate solutions.
Partner with traders and developers to implement research in production, monitor its performance and assess whether it delivers the expected trading impact.
Identify opportunities to turn manual trading judgements and heuristics into reproducible, measurable and scalable approaches.
Improve research methods and work with Technology to make models, tools and findings reusable across the business.
Communicate assumptions, uncertainty, limitations and commercial implications clearly, challenging existing thinking constructively and remaining open to challenge.
Share your expertise and support the development of other researchers, promoting rigorous, evidence-led decision-making.
Ensure research meets Maven’s risk, model governance, change-management and information-security standards.

What we’re looking for

A strong academic background in Mathematics, Statistics, Physics, Computer Science, Engineering, Operations Research or a related quantitative discipline.
A PhD or another demonstrable track record of conducting independent research.
At least three years’ experience in the financial industry, with relevant experience in electronic options trading.
Strong foundations in probability, statistics and quantitative modelling, with the ability to develop predictive models and test complex hypotheses using real-world data.
Proficiency in Python or another suitable research language.
Rigorous experimental and model-validation skills, including an understanding of uncertainty, overfitting and the challenges of translating research into live trading.
Commercial curiosity and the initiative to identify valuable problems, understand their significance and drive them towards a practical solution.
Sound judgement in balancing model sophistication, implementation complexity and expected trading value.
A collaborative approach, with the ability to connect quantitative research, trading knowledge and engineering expertise.
A strong sense of ownership, following ideas through implementation, adoption and measurable results.
Experience with AI tools for analysis, programming, or research.

Why join Maven?

Take substantial responsibility for research that directly shapes trading decisions and performance.
Work as an equal partner with experienced traders, researchers and engineers, with a meaningful voice in research priorities.
Develop and test ideas in a flexible research environment, with the opportunity to see your work deployed and measure its impact.
Extend the value of your research across multiple desks, products and markets.