Senior Data Scientist

Projects and Risk Management (PRM) Division will focus on the implementation of an effective project management standard to all strategic projects within the Club, and the rollout of the revised Club-wide Enterprise Risk Management Framework.

The PRM also serves as the Project Management Office for the Club’s major Strategic Programmes and Projects.

Its activities range from project execution, technical check and balance, creation of Operating Models and Project Risk Management.

You will:

• Support the establishment of the quantitative risk management team, methods and models to deliver advice to senior management and executives on risk detection and risk exposures by identifying opportunities to apply quantitative methods in the context of the Club’s Objectives and Risk Exposures, building specific models and quantitative approaches to detect, measure and monitor risk exposures and developing visualisation and dashboard solutions to report the advice provided by QRM outputs to management on a recurring basis
• Support the development of data sources and build data capabilities to provide optimised data capture processes linking with internal systems and external sources, data storage in an analysable and efficient format, data mining, engineering and cleansing approaches,and automation of data capabilities where appropriate
• Support the establishment of end-to-end machine learning capabilities and building machine learning and deep learning models, including model feature identification and engineering, selection of appropriate supervised and unsupervised machine learning techniques, identification of opportunities for deep learning applications
• Support the establishment of stochastic approaches to measure risk exposures for the purpose of i dentifying which risk exposures require management attention, incorporating risk quantification into strategic choice decisions and material business case decisions, assessing the return on investment of different control responses and understanding the amount of capital / contingency fund required to support costs of material incidents

You should have:

• Minimum of a post graduate qualification(s) in Data Science / Quantitative disciplines
• Minimum of 5 years of data science, actuarial, quantification experience, in large corporations, financial institutions or investment firms
• Knowledge of programming languages include: Python, R, VBA, C# or SQL
• Knowledge of Tools and Packages include: Pandas, Numpy, SciPy or similar
• Machine Learning/AI skills include: Linear/Logistic Regression, Random Forest, Gradient Boosting Decision Trees, Neural Networks and similar
• Knowledge of Stochastic modelling includes: Monte Carlo simulation

The level of appointment will be commensurate with qualification and experience.