Home Office

DAC 056 – Data Science

Incomplete applications

7
Incomplete applications
4 SME, 3 large

Completed applications

11
Completed applications
3 SME, 8 large
Important dates
Opportunity attribute name Opportunity attribute value
Published Thursday 23 November 2017
Deadline for asking questions Thursday 30 November 2017 at 11:59pm GMT
Closing date for applications Thursday 7 December 2017 at 11:59pm GMT

Overview

Overview
Opportunity attribute name Opportunity attribute value
Summary of the work A supplier to provide data science expertise, particularly the following areas:
• Development & Maintenance of Machine Learning models and information retrieval products.
• Delivery of data insight work (data analysis/exploration tools). Work with colleagues to prepare other tools for production. Ensuring that Master Data Management is fit for purpose.
Latest start date Monday 22 January 2018
Expected contract length 18 months
Location London
Organisation the work is for Home Office
Budget range

About the work

About the work
Opportunity attribute name Opportunity attribute value
Why the work is being done The Data Science service delivers data analytics products to transform Home Office operational businesses.

The Home Office requires data analytics to take advantage of the range of data held for the purposes of executing its responsibilities, making best use of that data to operate efficiently, ensure resources are focused on delivering high levels of customer service and maintaining security.

Data Analytics must also be exploited to inform policy development and operational decision making.

The Data Science service (working with existing civil servant resources) must ensure the Analytical Quality Assurance of analytics products that are delivered in the DACC.
Problem to be solved The supplier will bring expertise & experience in leading edge data science techniques.

The supplier will work in small scrum teams delivering analytics work, typically running 4-8 projects in parallel.

The supplier will bring expertise and experience in using leading edge analytical techniques and tools. Identifying where there is opportunity to add value by introducing new techniques, and scanning developments in industry and academia.

The supplier will work with business analysts, user researchers, product managers and customer product owners to ensure data products meet user needs

The supplier will ensure the Analytical Quality Assurance of all analytics products they deliver
Who the users are and what they need to do There are two core sets of users:

•End users of data analytics products, for whom data analytics offers an opportunity to transform their business or provide insight into policy or operational strategy

•Civil servant data scientists with whom the supplier will be in close collaboration in delivery of data analytics products, but for whom knowledge transfer and support are key deliverables.
Early market engagement
Any work that’s already been done The DACC is an established capability, a range of information retrieval tools and machine learning models are currently in discovery, alpha and beta phases. There is a growing controlled body of code and documentation stored on collaboration tools.

But there remains room to further improve our tooling / ways of working, and our production platform continues to evolve.
Existing team There is a permanent team of around 20 civil servant data scientists working alongside the current supplier team.

The current supplier team of data scientists will be available to support handover for a limited period, from the existing contract arrangement to the new.
Current phase Beta

Work setup

Work setup
Opportunity attribute name Opportunity attribute value
Address where the work will take place Work will be carried out from a Home Office location primarily in Croydon.
Working arrangements The supplier will be required to collaboratively work on site (5 days per week for stakeholder engagement).

Expenses for travel outside the M25 will be payable in accordance with the Home Office expenses policy.
Security clearance All staff must hold or be prepared to undergo SC Security Clearance to operate on this project. Some staff must hold or be prepared to undergo DV and/or NPVV3 clearance.

Additional information

Additional information
Opportunity attribute name Opportunity attribute value
Additional terms and conditions • Statements of work will be agreed periodically with the supplier on a fixed price, capped, or T&M basis with agreed deliverables
• Standard DOS framework and call-off terms and conditions.

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
  • Demonstrable skills of delivering data science products in to production using modern architectures (including with streaming data and relational & nosql databases) and open source tools (including Python, Spark).
  • Significant expertise in leading edge data science techniques, particularly machine learning, data linking, information retrieval, working with unsupervised data and data visualisation.
  • Demonstrable experience in successfully providing knowledge transfer to client teams.
  • Demonstrable experience of operating in a complex delivery environment, with experience of a significant contract within a large organisation in the last two years.
  • Business-focussed analysts, motivated to align delivery with the business strategy, with excellent client-facing and communications skills, and ability to quickly grasp business need and translate into analytical solutions.
  • Evidence of designing performance management (including valuing diversity and inclusion), resilience and capacity management into the service offering.
Nice-to-have skills and experience
  • Understanding of digital service design based on the government digital service standards, with evidence of working in agile delivery and delivering into cloud-hosted platforms
  • Experience in delivering data analytics using Greenplum, Elastic, Neo4j, IBM BigMatch, Tensorflow, Spark MLib
  • Experience of working within a central government ICT environment

How suppliers will be evaluated

How suppliers will be evaluated
Opportunity attribute name Opportunity attribute value
How many suppliers to evaluate 3
Proposal criteria
  • Approach and methodology
  • Technical / analytical skills and experience
  • Service model
  • Mobilisation plan
  • A plan for optimising resources
  • Identification of risks and dependencies
  • A plan for knowledge transfer
Cultural fit criteria
  • Ability to function effectively and collaborate in a multi-supplier environment
  • Working successfully in complex public sector organisations, influencing at senior levels
Payment approach Time and materials
Assessment methods
  • Written proposal
  • Work history
  • Presentation
Evaluation weighting

Technical competence

60%

Cultural fit

5%

Price

35%

Questions asked by suppliers

Questions asked by suppliers
Supplier question Buyer answer
1. What is the anticipated size of the team and how many resources per team will be required? We’re anticipating 10-15 data scientists for 18 months, but this will depend on the number of projects running in parallel.
2. Please can you provide further details on the size of team your require and the budget available? We’re anticipating 10-15 data scientists for 18 months, but this will depend on the number of projects running in parallel. It will be up to the bidder to say how much they believe they can deliver the service for.
3. Will the Data Science team be responsible for development of your "production platform" (and if so to what extent) or is the focus of that team solely on analytical/data products running on top of that platform. The data science team will be central to defining the requirements for our data science production platform, and working in collaboration with platform engineering and infrastructure teams to ensure that the platform is fit for purpose for data science work. But the core focus of the data science team will be on developing analytical/data products using infrastructure and tooling that are provisioned and maintained by others.