UK Research and Innovation – Science and Technology Facilities Council

UK SBS IT18162 UKRI STFC Machine Learning & Data Science Architect

Incomplete applications

11
Incomplete applications
9 SME, 2 large

Completed applications

4
Completed applications
4 SME, 0 large
Important dates
Opportunity attribute name Opportunity attribute value
Published Thursday 18 October 2018
Deadline for asking questions Thursday 25 October 2018 at 11:59pm GMT
Closing date for applications Thursday 1 November 2018 at 11:59pm GMT

Overview

Overview
Opportunity attribute name Opportunity attribute value
Specialist role Technical Architect
Summary of the work UKRI-STFC require a Machine Learning specialist to kick-start a new group’s infrastructure to look at Data Science for National Science Facilities.
Latest start date Monday 10 December 2018
Expected contract length 4 months (until end of March 2019) with the option to extend for up to an additional 3 months.
Location South East England
Organisation the work is for UK Research and Innovation – Science and Technology Facilities Council
Maximum day rate Up to £500 a day.

The total value of this contract shall not exceed £69,500.00 (excl VAT) including any options to extend.

About the work

About the work
Opportunity attribute name Opportunity attribute value
Early market engagement N/A
Who the specialist will work with The team is new and forming. There is a group leader (Prof Tony Hey) 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.
What the specialist will work on 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 at Rutherford Appleton Laboratory. 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.

Work setup

Work setup
Opportunity attribute name Opportunity attribute value
Address where the work will take place STFC Rutherford Appleton Laboratory, Harwell Campus, Didcot, OX11 0QX
Working arrangements The specialist will be based in the office five days a week and will be working with other colleagues by meeting, telephone, video conference and email where necessary. Standard hours are 9am until 5:30pm with an hour lunch break although there is flexibility. Travel will be seldom. Occasional remote working when necessary.
Security clearance Baseline Personnel Security Standard (BPSS) and Disclosure Scotland

Additional information

Additional information
Opportunity attribute name Opportunity attribute value
Additional terms and conditions T&S as per UKRI policy.

Skills and experience

Buyers will use the essential and nice-to-have skills and experience to help them evaluate suppliers’ technical competence.

Skills and experience
Opportunity attribute name Opportunity attribute value
Essential skills and experience
  • Demonstrate how you will apply your skills and expertise:
  • In 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
Nice-to-have skills and experience

How suppliers will be evaluated

How suppliers will be evaluated
Opportunity attribute name Opportunity attribute value
How many specialists to evaluate 3
Cultural fit criteria
  • Demonstrate how you will work with other people throughout this project – 5 points
  • Demonstrate how you will solve problems throughout this project – 5 points
  • Demonstrate how you will share knowledge and expertise throughout this project – 5 points
Assessment methods
  • Work history
  • Interview
Evaluation weighting

Technical competence

60%

Cultural fit

15%

Price

25%

Questions asked by suppliers

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