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Job Location | London |
Education | Not Mentioned |
Salary | Competitive salary |
Industry | Not Mentioned |
Functional Area | Not Mentioned |
Job Type | Permanent, full-time |
pricing and risk domains Test and develop Machine Learning / Deep Learning strategies using relevant methodologies to match the requirements Support scaling of current infrastructure; keeping abreast of the latest technologies, current business (data) model and relevant transformations required Working closely with the rest of the team in standardising codebase (for modelling pipelines) and data queries (defining variables) in the interest of speeding up deliveries Requirements: Quantitative background, gained through a combination of work experience and an advanced degree (MSc or PhD) in STEM Experience of using Python (associated ML/DL APIs - Numpy, Pandas, Scikit-Learn, TensorFlow / keras or PyTorch, PyMc3 is desired) Experience applying Machine Learning / Deep learning methodologies in relevant domains; Time Series Forecasting, predictive modelling, NLP, Computer vision or RL Ability to convert a non-technical problem description into a model / formal piece of analysis and explain derived insights to both technical and non-technical audiences Experience using one of Relational Databases (Oracle, Postgres, Sybase or SQL-Server) and/or exposure to high frequency tick programming software - HDF5, KDB+ or OneTick Desirable: Experience in distributed computing frameworks (Dask or Ray) and exposure to any of parallel programming frameworks (MPI, OpenSHMEM, Charm++ and Legion) Understanding of engineering best practices, agile development processes and version control Experience with containerisation and virtualisation (e.g. Docker, Kubernetes, VMs etc.)