Quantitative Analyst

Kindred Group plc

The role

The Quant Team is focused on developing quantitative sport models, taking models from prototype to production. The team is an important component of the vision for the future evolution of our Sportsbook Platform (KSP).

The Quant Team is split into two workstreams: Quant Research and Quant Engineering. We are looking to recruit a talented Quantitative Analyst to join the Research side to contribute to our sports modelling efforts. Your work will help deliver functionality, tools and models to support our broader objectives.

What you will do

• Hands-on contributor to sports modelling research and related product-driven workstreams.
• Research, develop and evaluate probabilistic pricing models across multiple sports.
• Contribute to product development, delivering independently once requirements are clearly set with relevant stakeholders.
• Carry out exploratory data analysis as part of modelling work, reporting back on findings and determining next steps.
• Contribute to model evaluation and backtesting to ensure pricing is of the highest possible quality.
• Work closely with the Quant Model Development team as part of efforts to productionise POC models and functionality.
• Work closely with the Quant Data Engineering team as part of efforts to build out data assets to support modelling work.
• Write code that follows best practices and facilitates collaboration and re-usability by other team members.
• Promote work and contributions for use within the team and the wider business, through conversations, reports, blog posts, showcases and presentations.
• Support junior members of the Quant Research team.

Expected Attributes

• Experience in a similar role in the sport betting industry.
• Knowledge of machine learning algorithms and/or statistics.
• Demonstrable experience applying statistical modelling techniques to sporting events.
• Intermediate programming skills in Python and SQL.
• Excellent skills manipulating, exploring and analysing data, including with libraries such as pandas, Numpy and Scipy.
• Experience interacting with databases.
• A problem-solving growth mindset with the ability to pick up new tools and concepts quickly.
• Able to interact in a constructive manner with colleagues in the team and with broader stakeholders.
• Proactive learner including desire to grow technical skillset and develop personally.
• Confident seeking advice and coaching from experienced team members.
• Well-organised and able to prioritise effectively.
• Able to communicate decisions and recommendations based on analysis.
• Ability to deal with uncertainty and flexibility to learn by iteration.

Desirable Attributes

• Masters/PhD in STEM subject.
• Understanding of functional and object-oriented programming paradigms.
• Experience with Bayesian models, Markov chains and multivariate time-series modelling.
• Experience with widely used probabilistic programming and machine learning libraries such as Stan, PyMC3, Scikit-Learn, Keras, Tensorflow or PyTorch.
• Exposure to cloud computing, ideally AWS.
• An interest in sports betting.