We are looking for a talented and highly motivated Quantitative Analyst for a unique and exciting opportunity to join our team building statistical and machine-learning models for developing predictive outcomes for sports wagering. We currently work in horse racing and football, but plan to develop these to include tennis, basketball and other sports. Previous exposure to developing ideas for models, writing code (e.g. using R, Python or MATLAB) and/or validating and deploying models would be an advantage. A BSc or an MSc in a relevant scientific field (statistics, data science, mathematics etc.) is preferred but would consider an undergraduate degree with the right experience and skills.
Investigate and develop statistical models for predictive outcomes in sports.
Collaborate with colleagues to back test, validate and deploy models.
Develop code.
Required
At least an BSc (MSc is preferred) in a relevant scientific field such as statistics, mathematics, data science etc., but we would consider an undergraduate degree for applicants with relevant experience (such as working in a current role in statistical modelling in sport).
Extensive experience of probabilistic and statistical modelling.
Strong programming skills in a high level language such as R, Python, MATLAB or Julia.
Ability to communicate results to those with and without specialist knowledge.
Fluent in English.
Desirable
Experience with database management.
Experience with machine learning algorithms, such as neural networks/deep learning, SVM, Random Forest, linear regression, Gradient Boosted Trees, etc.
Prior knowledge/experience with sports wagering or experience with automated trading systems.
Interest and a knowledge of some sports.
- Competitive compensation package.
- Bonus scheme.
- Participation in a lucrative profit share scheme.
- 25 days’ vacation a year plus all national holidays.
- Top range new hardware kit.
- Fully remote working.
- Flexible hours.
Send all relevant information (resume/CV and cover letter) in a pdf format by clicking on the "Apply Now" button.