Tender

Data Science Advice & Analysis

CARE QUALITY COMMISSION

This public procurement record has 1 release in its history.

Tender

30 Dec 2017 at 16:15

Tender Notice
Summary of the contracting process

The Care Quality Commission (CQC) is seeking proposals for the "Data Science Advice & Analysis" tender, aimed primarily at enhancing machine learning capabilities within their regulatory framework. The procurement is currently in the tender stage, with submissions due by 17:00 on 16 January 2018. The project location is London, United Kingdom, and it falls under the industry category of research and development services. The total contract value is £25,000, with a minimum value set at £15,000, and the contracted services are expected to commence from 2 February 2018 and run until 30 March 2018. The procurement method employed is a limited competitive quotation process, suitable for businesses offering services in consultancy and training in research and machine learning methodologies.

This tender presents an excellent opportunity for businesses specialising in data science, machine learning, or statistical consulting to engage with a leading regulatory body. Companies that have expertise in neural networks, support vector machines, and other advanced machine learning techniques, as well as those capable of delivering educational training, would be particularly well-positioned to compete for this contract. As the CQC works towards enhancing its analytical capabilities, businesses that understand the regulatory landscape and can demonstrate a strong track record in research and development consultancy will find themselves in a favourable position to successfully bid for this opportunity.

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Notice Information

Notice Title

Data Science Advice & Analysis

Notice Description

Our requirement is to gain a thorough understanding of, and practical experience in, applying machine learning methods and so we are seeking an expert with an understanding of our role as a regulator who is able to help us achieve this. We require the ability to have an ongoing dialogue with an advisor with the outcome that a small number of CQC staff have a good understanding of and are able to apply the methods by the end of the contract. The provider should have considerable experience providing machine learning advice and an excellent knowledge of academic research in this area and its practical applications. The provider needs to have an interest in, and knowledge of, the role of CQC. They should ideally understand the regulatory use of statistical methods and the framework in which we operate and should ensure that their advice is appropriate and stands up to public scrutiny should we choose to share it externally. There are two requirements. The first is specifically to advise on and discuss the applicability of a range of machine learning methods that are new to CQC. It is also to provide written materials in the form of a report and face to face training/lecturing suitable for the level of the Statistics team. The machine learning methods must include the following: * Neural networks * Support vector machines * Naive Bayes * Random forests, boosted trees Additional information: The requirement is set out in the Statement of Requirements of the tender document available at CQC e-Sourcing Portal: https://cqc-commercial.bravosolution.co.uk If your organisation does not already have an account on the portal, please select 'Register Free' and complete the company information to request a user name and password. On receipt of your account details, you may search for this opportunity by project name and select 'Register Interest'. To be considered as a Bidder you must complete and submit a Bid by the deadline of 17:00 16 January 2018 Please allow sufficient time to make your return as late returns will not be permitted by the system.

Notice Details

Publication & Lifecycle

Open Contracting ID
ocds-b5fd17-2f906ef7-a8ae-4b41-a58a-2747b799c65f
Publication Source
Contracts Finder
Latest Notice
https://www.contractsfinder.service.gov.uk/Notice/7bb31fd4-9e3b-459e-89ea-c0c279e2e827
Current Stage
Tender
All Stages
Tender

Procurement Classification

Notice Type
Tender Notice
Procurement Type
Standard
Procurement Category
Services
Procurement Method
Limited
Procurement Method Details
Competitive quotation (below threshold)
Tender Suitability
SME, VCSE
Awardee Scale
Not specified

Common Procurement Vocabulary (CPV)

CPV Divisions

73 - Research and development services and related consultancy services

CPV Codes

73100000 - Research and experimental development services

73200000 - Research and development consultancy services

73300000 - Design and execution of research and development

Notice Value(s)

Tender Value
£25,000 Under £100K
Lots Value
Not specified
Awards Value
Not specified
Contracts Value
Not specified

Notice Dates

Publication Date
30 Dec 20178 years ago
Submission Deadline
16 Jan 2018Expired
Future Notice Date
Not specified
Award Date
Not specified
Contract Period
2 Feb 2018 - 30 Mar 2018 1-6 months
Recurrence
Not specified

Notice Status

Tender Status
Active
Lots Status
Not Specified
Awards Status
Not Specified
Contracts Status
Not Specified

Buyer & Supplier

Contracting Authority (Buyer)

Main Buyer
CARE QUALITY COMMISSION
Contact Name
Available with D3 Tenders Premium →
Contact Email
Available with D3 Tenders Premium →
Contact Phone
Available with D3 Tenders Premium →

Buyer Location

Locality
NEWCASTLE UPON TYNE
Postcode
NE1 4PA
Post Town
Newcastle upon Tyne
Country
England
Major Region (ITL 1)
TLC North East (England)
Basic Region (ITL 2)
TLC4 Northumberland, Durham and Tyne & Wear
Small Region (ITL 3)
TLC43 Tyneside
Delivery Location
TLI London
Local Authority
Newcastle upon Tyne
Electoral Ward
Monument
Westminster Constituency
Newcastle upon Tyne Central and West

Further Information

Notice Documents

Open Contracting Data Standard (OCDS)

View full OCDS Record for this contracting process

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The Open Contracting Data Standard (OCDS) is a framework designed to increase transparency and access to public procurement data in the public sector. It is widely used by governments and organisations worldwide to report on procurement processes and contracts. 

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