Awarded to Cognizant Worldwide Limited

Start date: Monday 11 June 2018
Value: £2,000,000
Company size: large
Food Standards Agency

FSA574 - Call-Off Contract - Surveillance Programme - Use Cases

3 Incomplete applications

1 SME, 2 large

8 Completed applications

1 SME, 7 large

Important dates

Monday 5 March 2018
Deadline for asking questions
Monday 12 March 2018 at 11:59pm GMT
Closing date for applications
Monday 19 March 2018 at 11:59pm GMT


Summary of the work
Deliver predictive risk insight for use case using data science. Define use case exam question, establish potential enabling data sources and high level data scoring, such as perceived value, reusability, accessibility, veracity etc. Collate and prepare data, run hackathons, refine analytical models, validate insight, package outcome for adoption/rollout.
Latest start date
Monday 16 April 2018
Expected contract length
24 month call-off agreement during which time individual Work Packages will be commissioned.
Organisation the work is for
Food Standards Agency
Budget range
The indicative budget range for the two year call-off agreement is £1.6M to £1.8M. The FSA will not guarantee any minimum spend value under the proposed call-off agreement

The indicative budget for the first work package is £100,000 to £120,000

About the work

Why the work is being done
FSA Surveillance programme encompasses the development of a new strategic approach to surveillance which meets the WHO definition of the ongoing systematic collection, collation, analysis and/or interpretation of data, followed by dissemination of information so that directed action may be taken. The surveillance programme is strongly aligned to the FSA strategic objective of ensuring that food is safe and is what it says it is. It is also aligned to the corporate objectives of preparing the FSA for Britain’s exit from the EU as well as the vision to better Regulating Our Future, such that we are largely data driven.
Problem to be solved
Factors such as population growth and climate change contribute towards increasing risk of food safety. FSA utilises numerous approaches to mitigate this risk, however, FSA recogises the need to establish complemtary approaches to predictive risk identification. Surveillance will use linked data sources (FSA data, Social, Open data, Commercial etc) with advanced analytics and data science to predict risk and enable data informed decisions for intervention. It is also aligned to the corporate objectives of preparing the FSA for Britain’s exit from the EU as well as the vision to better Regulating Our Future, such that we are largely data driven.
Who the users are and what they need to do
As a FSA colleague…
I need access to predictive risk indicators

So that…
I can make data decisions and take informed interventions

As Surveillance lead…
I need a flexible Surveillance ecosystem upon which colleagues can make timely decisions and take interventions.

So that..
Surveillance supports the FSA’s strategic objective of ensuring that food is safe, and contributes to the preparation for Britain’s exit from the EU.

As a data scientist…
I need a toolbox of reusable and extendible analytical models

So that…
I can experiment with models efficiently and effectively, provide valuable insight using open data and open source tools
Early market engagement
Any work that’s already been done
Vision and high level operating model design of Scan, Spot, Narrow and Evalute defined. Two proofs of concepts completed in parallel over time-boxed 7 weeks resulting in PoC's dashboards and adopted outline delivery approach of business use case and exam question definition, data source identification, data collation and preparation, hackathon/exploration, analytical model validation and enhancements, outcome interpretation and stakeholder playback. Commissioned first iteration of Surveillance process design and implementation to map end-to-end process and enable repeatable data/method derived decision making on which use cases to approve and prioritise.
Existing team
Whilst working closely with the FSA Surveillance team, the supplied team is expected to encompass the level of resources, skills and experience necessary to assume the lead role with ownership for successful planning, delivery and outcomes.
The internal team consists of a lead digital consultant/pgm mgr, FSA Surveillance lead, two operational researchers from the Analytics Research Unity (consisting of one principal & one fast streamer), FSA Data Scientist, Use-case capture and support, Official Veterinarian (general support role)
The FSA Surveillance team is also supported with director level SRO sponsorship.
Current phase

Work setup

Address where the work will take place
FSA London office, Clive House, 70 Petty France, Westminster, SW1H 9EX
Working arrangements
Primary location will be co-located with the Surveillance team in London. Flexibility is also necessary as this initiative may also require occasional face to face off-site workshops and distributed working supported with digital planing and collaboration tools, such as Trello and Slack.
Security clearance
All supplier team members will need to be cleared to Baseline Personnel Security Standard, and sign a non-disclosure agreement (NDA)

Additional information

Additional terms and conditions
This is a 24 month single supplier call-off agreement during which time individual Work Packages will be commissioned by the FSA.
Further details regarding the scope of requirements over the 24 month call-off period can be found here -

Details of the first Work Package can be found here

There is the potential for the commissioning of up to a further 5 work packages in the 2018/19 financial year. Subject to budgetery approval a similar number of work packages may be commissioned in year two.

Skills and experience

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

Essential skills and experience
  • Experience of working with multidisciplinary teams, promotes and applies innovative approaches to resolve challenges, ensuring we do not simply digitise existing methods. Presents compelling findings to clearly inform stakeholders.
  • Practitioner level data science experience and understanding how algorithms are designed, optimised and applied at scale. Can select and use appropriate statistical methods for sampling, distribution assessment, bias and error.
  • Demonstrable experience of problem structuring methods and can evaluate when each method is appropriate. Applies scientific methods through experimental design, exploratory data analysis and hypothesis testing to reach robust conclusions
  • Experienced in how to build scalable machine learning pipelines and combine feature engineering with optimisation methods to improve the data product performance.
  • Demonstrable understanding of how to expose open data from systems (for example, through APIs), linking data from multiple systems and delivering data pipeline streaming services.
  • Experience of planning and executing rapid prototyping such as hackathon events in mixed environments, providing clear guidance and support to ensure successful outcomes are reached.
  • Experienced in using the most appropriate medium to visualise data to tell compelling and actionable stories relevant for business goals.
  • Maintains user focus to design solutions that meet user needs.
  • Recognises and exploits business opportunities to ensure more efficient and effective ways to use data science. Proficient at developing and applying approach toward reusable and transferable components.
  • Highly proficient in developing capability roadmaps, ensuring clear understanding for non-technical stakeholders combined with clear action path to achieve agreed milestones.
  • Demonstrable experience of working within an agile methodology while retaining the ability to recognise and work with waterfall governance structures. Applying appropriate aspects of each methodology to maintain delivery pace.
  • Detailed understanding of government digital service standard and Technology Code of Practice, including GDS service design manual and wider industry-standards, identifying and applying elements beneficial to FSA Surveillance.
  • Experience of defining appropriate portfolio, programme and project structures in the context of digital services delivery, and providing reporting, planning, risk, dependency, financial and change management aspects of the Programme.
  • Understanding of possible technological developments for data science, identifying and leveraging opportunities for our programme of work.
Nice-to-have skills and experience
  • Demonstrate an understanding of possible technological developments within the food industry and the opportunities this offers our programme of work.
  • Experience of defining appropriate portfolio, programme and project structures in the context of digital services delivery, and providing reporting, planning, risk, dependency, financial and change management aspects of the Programme.
  • Designing and implementing solutions that comply with the General Data Protection Regulation (GDPR) (Regulation (EU) 2016/679)

How suppliers will be evaluated

How many suppliers to evaluate
Proposal criteria
  • Approach and methodology
  • How the approach or solution meets your organisation’s policy or goal
  • Estimated timeframes for the work
  • How they’ve identified risks and dependencies and offered approaches to manage them
  • Team structure
  • Value for money
  • How the approach or solution meets user needs
Cultural fit criteria
  • Experience of successful collaborative working as part of a multi supplier delivery team sharing knowledge within the team, and be transparent and collaborative when making decisions
  • Able to challenge the status quo and use persuasion skills to achieve business buy-in
  • Bring agile mindset and principles into practice.
  • Ability to engage with various types of stakeholders, understanding their level of technical literacy and adapting behaviour/communications accordingly
Payment approach
Capped time and materials
Assessment methods
  • Written proposal
  • Case study
  • Work history
  • Reference
  • Presentation
Evaluation weighting

Technical competence


Cultural fit




Questions asked by suppliers

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