We are looking for a data scientist familiar with a broad spectrum of modelling techniques (both classical ML and various deep learning modelling approaches) utilising a wide variety of data types (e.g. transactional financial data, system log data, clickstream data, geospatial data, customer support chat log, audio recordings, image and video data, etc) to be able to build models that support:

● Digital banking product features such as smart financial recommendations around how much to save when
● Business operations optimisation
● Fraud detection and other risk management functions
● Improving the efficiency of various technical operations with the business

The focus will be more on design and building the models than on ops. A machine learning engineer will support you together with a data science platform team when it comes to operationalising models within a production environment.


Strong communication skills are essential to help communicate complex ideas to less technical audiences.


Experience with financial modelling of moderate complexity is highly desirable as some of the features that will be built out will involve forecasting and support personal financial management product features.


Requirements:
● A MSc or PhD in computer science, statistics, physics, mathematics or other related degree. Ancillary degrees or courses in finance, economics, actuarial and related disciplines would be valued.

● Understands the theoretical foundations underpinning machine learning and deep learning models while also has hands-on experience dealing with the problems they throw up in the real world.

● 1+ years experience deploying machine learnings in production environments

● 3+ years building machine learning and deep learning models.

● Strong familiarity with tools within the PyData ecosystem such as Numpy, Scipy, pandas, scikit-learn, PyTorch, Tensorflow, spaCy, etc

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