Quantitative Lead - IT Projects & Programmes - Gaming Industry

To provide a senior level of direction and hands on skills for the analysis and build of industry leading solutions to support company's Proprietary trading platform and tools, this will involve the build and maintenance of mathematical models to enable company to deliver robust prices and odds for all sports.

Key Responsibilities :

- Build, implement and maintenance of new and legacy predictive models harnessing probability calculations and algorithms based on sound mathematical principles.

- Work with Trading teams, key stakeholders, and the product managers to understand requirements for trading and risk management tools

- Demonstrate technical skills and abilities with technologies used to

- Create/feed into business cases for technology and track delivery of benefits, working with Trading and delivery partners as appropriate.

- Work closely with counter parts in trading, technology, and other product areas to assist with service evolution and implant quantitative knowledge and awareness.

- Act as a conduit between Trading, IT, and other key stakeholders being able to explain mathematical models to non-mathematicians such as Trading Operations, Client Risk etc. in the delivery of trading and risk management tools.

Key Accountabilities:

- Represent the Trading business on IT projects and Programmes, and in dealing with 3rd party suppliers, ensuring the delivery of Trading goals remains paramount

- Ensure effective regular communication with IT and other parts of the business about delivery and impacts of trading and risk management tools

- Develop and maintain key relationships with the Product Managers, IT, ensuring the appropriate level of stakeholder engagement

- University degree (ideally Masters / Ph.D.) in Mathematics and/or some field in applied mathematics such as statistics, control theory or theoretical physics.

- Ideally, you've already worked with quantitative finance modelling or similar role within the gaming industry or exposure to a trading department where modelling / quant data research has been practiced.

- Experience in finding patterns in data and applying machine learning techniques to derive models.

- Knowledge of Mathematica, R (or some other statistical software), SQL and F# (.Net & OOP) is an advantage.

An understanding or knowledge of trading systems including probabilities and the algorithmic approach as applied to certain sports