We require a machine learning engineer to work together with the data science team to design, build, train, optimise, deploy and monitor models in production environments. You will work alongside a data science platform team who have built tools for creating feature stores and easing deployment of models to production, but will be the main individual responsible for ensuring models are successfully used in production. You will also work closely with data engineering, product and software engineering. A mature approach to being able to balance technical and business concerns in a pragmatic manner that respects long term business objectives is required.
The models will need to be served in an online fashion and operate at large scale with low latency. They will support a number of capabilities including:
- Digital banking product features such as smart financial recommendations around how much to save when
- Business operations optimization
- Fraud detection and other risk management functions
- Improving the efficiency of various technical operations with the business
Exposure to retail banking is desirable.
- A computer science / informations graduate degree with a strong background in mathematics and statistics
- A sound demonstration of both the theoretical foundations underpinning machine learning and deep learning models as well as hands on experience dealing with the problems they throw up in the real world.
- 3+ years software engineering experience with at least one year deploying machine learning models in production environments
- 3+ years experience with big data / distributed processing systems
- Strong familiarity with tools within the PyData ecosystem such as Numpy, Scipy, pandas, scikit-learn, PyTorch, Tensorflow