Junior Quantitative Researcher – Sports Betting/ London/ 75K job at London Full Time

A leading sports betting fund is seeking a Junior Quantitative Researcher to join its expanding quantitative research team. This is an exciting opportunity for an analytically minded individual with a passion for sports modelling, data science, and statistics to contribute to cutting-edge research and model development within a high-performing environment.

Key Responsibilities

1. Assist senior quantitative researchers in delivering research and model development projects.
2. Support clients and internal teams by:
3. Developing, maintaining, and improving the mathematical libraries that power predictive models and analytical tools.
4. Building and maintaining software systems that deliver model outputs into production.
5. Perform statistical analysis of datasets, test hypotheses, and communicate findings effectively to key stakeholders.
6. Contribute to the ongoing enhancement of core programming libraries.
7. Participate in at least one professional development event annually—such as a conference, workshop, or networking event—focused on areas like sports analytics, statistics, machine learning, or gambling.

Skills & Experience

Required

1. MSc in Statistics , Data Science , Mathematics , or another quantitative discipline (e.g., Computer Science, Engineering, Finance) with a strong statistical component.
2. Prior experience in a role involving significant statistical analysis, demonstrating skills beyond academic study.
3. Programming experience and a willingness to learn and work in R .
4. Demonstrated passion for sports modelling—through personal projects, academic research, or independent analyses.
5. Commitment to continuous learning and professional growth.
6. Curiosity and enthusiasm for exploring new technologies and programming languages.
7. Eligibility to work in the UK .

Preferred

1. Strong interest in horse racing , supported by prior modelling or data analysis projects.
2. Understanding of sports betting markets .
3. Familiarity with statistical and machine learning methods (e.g., GBM, Torch, CNN, LSTM, NLP, GNN).
4. Experience with additional programming languages (e.g., Python, C++, Julia).
5. Working knowledge of database systems (e.g., SQL, MongoDB, Redis, Postgres).
6. Experience with version control , code reviews , and merge requests .
7. Familiarity with CI/CD pipelines and test-driven development (TDD) .