Some of the key responsibilities include:

• Prepare and analyse and interpret large complex data for modelling business problems.
• Implement or reuse the existing Machine Learning algorithms on multi-modal data.
• Provide analysis and prototype solutions for the business (e.g., by translating complex commercial problems to Machine Learning problems).
• Be an active member of teams that provide the business with data-driven apps, insight and strategies.
• Communicate findings and deliverables with colleagues and stakeholders.

What you’ll need to succeed

• A postgraduate qualification in a numeric discipline such as Data Science, Statistics, Machine Learning, Computer Science, etc.
• Previous experience in a Data Science role within Financial Services
• Practical experience in preparing data for statistical analysis and/or Machine Learning (e.g., using SQL and/or NoSQL, Pandas, or other technologies).
• Scientific expertise and applied experience in Machine Learning with an understanding of common Machine Learning algorithms
• Knowledge of advanced statistical theories, methodologies, and inference tools (e.g. familiar with hypothesis testing, (generalized) linear models, additive models, mixture models, non-parametric models, etc.).
• Python coding and programming experience (data processing, model training, model serving, and data visualization etc.).
• Understanding of the Microsoft Power Apps suite of products.