At a Glance
- Tasks: Design and deploy automated trading strategies in live markets.
- Company: Join a respected quantitative research team in sports and trading.
- Benefits: Competitive salary, hybrid working, discretionary bonus, and excellent benefits.
- Other info: Collaborate with top minds and tackle challenging quantitative problems.
- Why this job: Make a real impact with your research in a dynamic trading environment.
- Qualifications: Experience in quantitative research, algorithmic trading, or statistical modelling.
The predicted salary is between 80000 - 120000 £ per year.
Python | R | Quantitative Research | Market Making | Algorithmic Trading | Betting Exchange | Sports Betting | Statistical Modelling | Trading Strategies | Forecasting | Do you want to work in an environment where your research is tested against live markets every day?
A role where you'll design and deploy automated trading and market‑making strategies rather than simply build models that sit on a shelf?
An opportunity to work alongside some of the brightest quantitative minds in sports, trading and statistical research?
This is the opportunity to join a highly respected quantitative research team developing predictive models and automated execution strategies across multiple global sports markets.
Working within the Execution Research team, you'll focus on market making, pricing, trading signals, order book dynamics, customer behaviour and the optimal execution of trading strategies across both pre‑match and in‑play markets.
You'll be given the freedom to take ideas from research through to production, developing robust quantitative models and deploying them into live environments using Python and/or R.
This is a highly academic environment with peer reviews, research discussions and continuous learning, but unlike academia, you'll receive immediate feedback from real-world market performance.
Particularly interested in candidates with backgrounds in quantitative research, algorithmic trading, market making, betting exchanges, sportsbooks, financial trading or execution research.
Experience with probabilistic modelling, Bayesian methods, machine learning, reinforcement learning or optimisation techniques would be highly beneficial.
The role offers £80k - £120k - hybrid working options, plus discretionary bonus, excellent benefits and the opportunity to work on genuinely challenging quantitative problems alongside a talented team of researchers and engineers. #J-18808-Ljbffr
Contact Details:
Opus Recruitment Solutions Recruitment Team
StudySmarter Expert Advice🤫
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