Machine Learning / Data Science Architect

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Machine Learning and Data Science Architect, Machine Learning Specialist, Machine Learning Consultant, Machine Learning Designer, Machine Learning Architect, Data Scientist, Data Science, Data Science Architect, ScikitLearn, TensorFlow, Azure, Dataset, L Toolkit, Statistical Models, Computational Experiments, Public Sector, Didcot, Oxford

Machine Learning & Data Science Architect, Public Sector, 4+ Months, Outside IR35, Didcot, Oxfordshire

Our client require a Machine Learning specialist to kick-start a new group's infrastructure to look at Data Science for National Science Facilities.

The team is new and forming. There is a group leader and potentially a senior data scientist will be on board by the time the specialist commences works. Two more group members start in October while the specialist will be working with the owners of the datasets from the national science facilities and at universities.

You will be working to set up a Science & Machine Learning group area in the Scientific Computing Cloud and the Azure Cloud, with a range of different datasets for the different types of experiments. The objective is to make a range of Machine Learning L toolkits - Microsoft's Machine Learning suite, ScikitLearn, TensorFlow etc. - available and record the results of the different methods on different datasets and Cloud hardware in a set of files. Setting up these benchmark datasets together with some specimen results will be the major part of the work.

Key skills / experience

* Data science and machine learning and how this will ensure the successful delivery of this project - 15 points

* To manage, structure, and analyse data, including building statistical models and using machine learning technologies and how this will ensure the successful delivery of this project - 15 points

* To utilise machine learning toolsets such as SciKit Learn, TensorFlow, etc. and how this will ensure the successful delivery of this project - 15 points

* To manage and organise the parameters and results of computational experiments and how this will ensure the successful delivery of this project - 15 points