Data Science Internships

A list of our currently available projects can be found here

Overview

This scheme aims to connect NHS real data and issues with academic thought and research through short-term PhD internships. The benefit to the NHS is the added value that academia brings to evidence-based research but on timescales that allow the insights to be acted upon. For the student and university, the benefit is an avenue to conduct related research in an industrial environment and access to NHS data.

We are looking for PhD students working in a quantitative discipline but with an interest in applying their knowledge and gaining experience of creating solutions for the NHS. Both student and university will be funded during the duration of the internship covering between three and six months.

Our aim is to continually build on previous learning whilst having an avenue for including the latest research and approaches. Where possible we will work in an open and transparent way ensuring that learning is shared and insights made available for others to reproduce. At the end of the project the applicant will submit a final report suitable for publication in open literature, and presentations to NHS England on their results including their experience of the project. The nature of the output will depend on the specific project.

Key priorities of the internship will be:

• Safe and appropriate use of NHS data
• To producing a balanced outcome for both the student and NHS England, with useable outputs
• To provide the student(s) with experience of completing a live business project
• To provide the NHS with an avenue to experience current research and ideas
• To kick-start or accelerate current projects and ideas
• To build a long term programme with developing research areas

The Role

The successful candidate will occupy a permanent role within the Digital Analytics and Research Team (DART). During the internship the candidate will be expected to progress their chosen project autonomously with supervision from colleagues in the DART as well as their current academic supervisor. The candidate will be expected to be focussed on the project and self-driven during the internship period, providing regular updates on progress and issues.

Within the topic area of the chosen project, there will be some freedom to direct the development of the research and knowledge but this will also need to be balanced against creating a learning outcome or tangible output, that benefits the PhD scheme objectives and is in a shareable state for future projects to pick up and continue the development.

As the candidate will be a NHS England employee during the internship period, standard employment checks and some mandatory training are required.

Key aims for the intern will be:

• Progress the research project ensuring learning/outcomes are shareable with NHS England and where appropriate made suitable for publication.

Projects

A list of currently available projects can be found on the DART site with links to project specific details on github. These projects are continually being added to and we welcome project proposals from both prospective students and interested organisations.

During the Project Period, the Student will be allocated an NHS England supervisor and in some instances a project specific mentor. It is also required that an academic supervisor from the hosting university is identified before the student can accept the internship place.

Funding

For the entirety of the internship, the applicant would be paid as a NHS band 6 (spine point 1) post according to NHS Terms and Conditions (AfC) pay scales - Annual

Application Process

Eligibility criteria

Applicants will need:

• to provide acceptable proof of legal right to study in the UK or ability to satisfy the current requirements of UK Visa and Immigration.
• to be enrolled in a PhD programme in a qualitative field of study (such as mathematics, statistics, data engineering, computer science, data science, physics, …) during the course of the internship.
• to provide a statement of current PhD supervisor support stating that the student is able to conduct this work alongside their study or that the student will take a break from their current funding whilst conducting this work

Timelines

Applications Open - Early October

Applications Close - Early November

Interviews - Late November

Outcomes Communicated - Early December

January Wave Start - Mid-January

June Wave Start - Mid-June

How to Apply

Application links will be made available on the DART website between the open and close dates

As part of the application you will be asked to specify which project(s) you are interested in and any relevant experience in these areas.

Applications will be sifted using a set of sifting questions to ensure the candidate fulfils the minimum requirements for this role. Each question will be marked by three sifters using a score of 1 to 5. The sifters will not be able to see any other material the candidate has provided whilst marking each individual question so please ensure each answer doesn’t refer to other materials and that all information and evidence is self-contained in the question.

Interviews - The interviews will be conducted as a panel with competency based questions. During the interview process, candidates will be asked to provide further evidence of their interest, technical skill and experience. You will also be asked to provide a statement of support for the internship from your PhD supervisor.

Ways of Working - Project Structure

As these are short-term and remote projects we propose a minimum of weekly project check-ins with the NHS project supervisor. The frequency can be increased if the project has a specific need. Code reviews are encouraged as this increases the quality of code as well as ensuring the NHS supervisor understands the detailed progress of the work. These meetings will focus on technical specifics and hurdles.

More formal milestone meetings are also requeted in order to run the project past the innovation team for wider comment. These meetings will focus on deliverables and project timeline.

It is expected at the beginning of the project that these milestones will be agreed between candidate and supervisor, and put in place to support monitoring of the project timelines, but should reflect releases of product / stages of the project where possible.

Generally, we expect that the first couple of sprints are spent on background reading and scoping out the project (as well as some mandatory training). We also use this time for data access and environment setup. The project then usually moves to an exploratory phase before coming back to the project scope and finalising the main aims based off the exploration learning. The final couple of sprints should ensure that enough time is left for code tidy-up and write-up as we recognise the importance and time taken to generate materials that clearly describe the work and outcomes as well as creating open assets that others will be able to pick-up and run themselves.

Ways of Working - Project Outputs

At the end of the project, we would look to have the following outputs alongside the codebase generated during the project:

Mandatory

Project Code

The key output of the project is the code. We ask that where possible this code is made open through github. This github repo template is encouraged for interns to use.

We will also ask all our interns to be aware of and follow our coding in the open guidance and checklists to ensure that the code released is appropriate.

Technical Report

To enable a future user to understand the code and learning. Expect the reader to have some basic knowledge of the area and to have access to the repo with the final code in. Reference placeholders in the code and include code/project story to help reader understand the “why have they used this approach” as well as the what.

Suggest around 2-pages per month but depends on project need.

Project Overview Slides

5 - 20 slides with a focus on describing the project aims and achievements

Optional

Experience Blog (to go on technical store)

Half page description of internship experience with learning/feedback. Optional to do this as a blog but we will need to capture some feedback please.

Technical Blog (to go on technical store or sit as an io page on the repo)

Focus on method/algorithm (e.g. Choice of sentiment library, VAE-DP, GPU requirements etc…) and acts as a stand alone explanation to others interested in the technique around it’s nuances and use.

Recorded run through

To support others to understand the project a 5-min recorded demonstration or talk could be considered.