Quantitative Analyst required by a sports trading company based in central London. The company have been established for more than 10 years, successfully developing statistical models and analytical frameworks. The successful Quantitative Analyst will work within the prediction team, using extensive datasets to enhance existing predictive models as well as researching new methods. The role will be working alongside … a team of Quants, Developers, and Analysts. Due to the nature of the business this is mainly an office-based role. Experience required: 3+ years' experience within predictive modelling, machine learning, and probability theory. Ideally this would be within sports or gaming/betting industries. Understanding of techniques such as Monte Carlo simulation, Bayesian modelling, GLMs, mixed effects More ❯
collaborate with cross-functional teams to design, build, and implement data science solutions that directly feed into core product offerings. This is an exciting opportunity to apply ML and statistical techniques to real-world problems, while also developing in areas such as software engineering, data pipelines, and ethical product delivery. Key Responsibilities Collaborate with product teams to identify opportunities … maintain data pipelines to support experimentation and model development Develop and deploy machine learning models in production environments Present insights and results to technical and non-technical stakeholders Apply statistical methods and model validation techniques to reach accurate conclusions Work with data engineers to ensure clean, usable datasets Contribute to ethical, privacy-aware, and user-centric data products Stay More ❯
collaborate with cross-functional teams to design, build, and implement data science solutions that directly feed into core product offerings. This is an exciting opportunity to apply ML and statistical techniques to real-world problems, while also developing in areas such as software engineering, data pipelines, and ethical product delivery. Key Responsibilities Collaborate with product teams to identify opportunities … maintain data pipelines to support experimentation and model development Develop and deploy machine learning models in production environments Present insights and results to technical and non-technical stakeholders Apply statistical methods and model validation techniques to reach accurate conclusions Work with data engineers to ensure clean, usable datasets Contribute to ethical, privacy-aware, and user-centric data products Stay More ❯
