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 the FDJ United Sportsbook Platform. 

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

Responsibilities

• Play a key technical role in researching, developing and refining probabilistic pricing models across multiple sports, including (but not limited to!) basketball and tennis.
• Contribute to the development of BetBuilder / same game accumulator models for these sports.
• Expand existing capabilities for modelling player props across multiple sports, including football and basketball.
• Take a lead role in scaling the Quant Team's ability to evaluate and/or backtest sport models.
• Own technical aspects of model development, interpreting requirements and delivering independently whilst maintaining alignment with senior team members.
• Solve challenging problems where multiple potential solutions exist.
• Make key technical contributions to tools that assist traders in offering odds to customers.
• Work collaboratively with Quant Model Engineers to productionise models.
• Work collaboratively with Quant Data Engineers to build out data assets.
• Build strong relationships with System Engineers and Architects.
• Write high-quality 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.
• Coach and support members of the Quant Research team, helping them up-skill and setting examples through work and behaviours.
• Dedicate time for investigating new techniques and methodologies that can benefit the team and wider business.
• Ensure that you adhere to the Governance, Risk & Compliance (GRC) obligations for your role.
• Identify and raise any non-compliance incidents promptly to your line manager.
• Challenge processes, policies and projects that will negatively impact compliance within the Group.
• Complete all mandatory compliance training assigned to you.
• Reach out to the Compliance Teams if unsure of any of your compliance obligations or the requirements are unclear.

Expected Attributes

• Extensive commercial experience in similar sports modelling roles, with a track record of delivering algorithmic data-driven products to production.
• Extensive knowledge of machine learning algorithms and tooling.
• Experience with Bayesian models, Markov chains and multivariate time-series modelling.
• Advanced progamming skills.
• Experience deploying code within production environments and applying software development best practices such as version control, unit testing, linting and CI/CD.
• Experience deploying software within cloud-based environments, ideally AWS.
• An excellent communicator, both written and verbal, able to explain complex topics to non-specialists.
• Well-organised with the ability to make well informed decisions based on data and to prioritise effectively
• Confidence to make clear recommendations to support decision making, e.g. around project priorities and planning.
• Able to interact in a constructive manner with colleagues in the team and with a broad range of stakeholders with capability to move up and down the levels
• Able to operate in a fully autonomous manner.
• Ability to deal with uncertainty and flexibility to learn by iteration.

Desirable Attributes

• Masters/PhD in STEM subject.
• Experience with using simulation approaches for sports models, especially within the context of BetBuilder products.
• Experience with player prop modelling.
• Experience with probabilistic programming and machine learning libraries such as Stan, PyMC3, Scikit-Learn, Keras, Tensorflow or PyTorch.
• Experience with big data technologies such as Spark.
• Programming skills in a statically typed language such as Scala, C++, Rust or Java.
• Understanding of software design patterns.