White Swan Data

Data-driven betting models; liquidity provider

Quantitative Trading Specialist

Quantitative Trading / Algorithm DevelopmentUpdated on Nov 13

Full-timeLondon, UKNo salary listedIn-person

About the Job

Requirements

Candidates must have a keen interest in sports and demonstrate significant domain knowledge, including sports context and knowledge of betting markets.
Candidates must have a strong trading instinct and comfort making pricing and risk decisions under time pressure and uncertainty.
Candidates must know sports rules, player dynamics, team strategies, and how betting markets behave.
Candidates must have a sound quantitative and statistical foundation to interrogate models, understand their assumptions, and know when to trust or override them.
Candidates must have strong attention to detail and the ability to retain and act on information quickly.
Candidates must have strong communication and collaboration skills.
Candidates must have the ability to meet deadlines and willingness to learn and adapt to new environments.

Responsibilities

Trade sporting markets live, pre-match and in-play, taking ownership of pricing, positioning, and risk management in real time.
Set and adjust prices in response to market moves, news, and in-game events, balancing competitiveness against edge.
Turn value signals into executable trading strategies and refine them based on live performance and market feedback.
Back test and validate strategies before deployment, then operationalise them into automated or semi-automated trading workflows.
Monitor markets and model outputs live, spotting mispricing, adverse selection, and opportunities as they emerge.
Work closely with quants and engineers to translate trading intuition into models, tooling, and process improvements.
Contribute to the acquisition, cleaning, and interpretation of data where it supports pricing and trading decisions.

Desired Qualifications

Experience trading or market-making in betting, gaming, or financial markets, especially in-play or live trading.
Proficiency in an analytical programming language such as Python or R.
Proficiency in production-ready engineering, including architecture, low-latency systems, Java, or C++.
A BSc in mathematics, statistics, computer science, engineering, or another quantitative discipline.

We're expanding a successful sports quant strategy. We're looking for quantitative analysts and traders who have the skills to identify value signals, develop strategies to exploit those signals, back test them, operationalise those strategies, and manage risk live in real time. The ideal candidates will be able to deliver all stages of the strategy realisation pipeline, but we will consider candidates who are able to demonstrate expertise in some parts of it.

Candidates must have a keen interest in sports and demonstrate significant domain knowledge, with sport context and knowledge of betting markets.

Trade sporting markets live, pre-match and in-play, taking ownership of pricing, positioning, and risk management in real time.
Set and adjust prices in response to market moves, news, and in-game events, balancing competitiveness against edge.
Turn value signals into executable trading strategies, and refine them based on live performance and market feedback.
Back test and validate strategies before deployment, then operationalise them into automated or semi-automated trading workflows.
Monitor markets and model outputs live, spotting mispricing, adverse selection, and opportunities as they emerge.
Work closely with quants and engineers to translate trading intuition into models, tooling, and process improvements.
Contribute to the acquisition, cleaning, and interpretation of data where it supports pricing and trading decisions.

Strong trading instinct and comfort making pricing and risk decisions under time pressure and uncertainty.
Knowledge of sports rules, player dynamics, and team strategies, plus a solid grasp of how its betting markets behave.
Sound quantitative and statistical foundation, enough to interrogate models, understand their assumptions, and know when to trust or override them.
Strong attention to detail and the ability to retain and act on information quickly.
Strong communication and collaboration skills.
Positive 'can do' attitude and the ability to meet deadlines.
Willingness to learn and adapt to new environments.

Nice to have skills:

Experience trading or market-making in betting, gaming, or financial markets (in-play or live trading a strong plus).
Proficiency in an analytical programming language (Python, R or similar).
Proficiency in production ready engineering (Architecture, low latency systems, Java or C++)
BSc in mathematics, statistics, computer science, engineering, or another quantitative discipline.

About the Company

White Swan Data

Data-driven betting models; liquidity provider

White Swan Data is a London-based firm that builds data-driven betting models to identify opportunities in iGaming and prediction markets. It provides mathematics and data-led services to price risk and develop betting strategies, and acts as a market maker and liquidity provider for prediction exchanges and related platforms. It quotes prices, absorbs event risk, and supplies liquidity to deepen markets, sometimes handling a large share of activity on secondary exchanges. Its team of mathematicians, data scientists, and software engineers builds the infrastructure to support gambling and trading services, aiming to deliver reliable liquidity and scalable trading systems for clients.

Company size: 51-200
Company stage: Not listed
Total funding: Not listed
Headquarters: London, United Kingdom
Founded: 2019

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