# Turing REG Recruitment - FAQ ## Application Stage ### Is the position open to applicants outside of the UK who do not currently have a UK working Visa but could obtain one? Yes, the positions are open to applicants outside the UK and our HR Team (HR@turing.ac.uk) will be able to offer you additional information on the process of obtaining a working Visa. ### Are you accepting remote applicants? Our team is currently trialing being remote-first and is minimising the amount of time people will be required to be in the office. Requirements for in-office attendance will depend on projects, but we will seek to match these to individual preferences wherever we can, and no team member will be asked to be in the office more than one day per week during the trial period. Note however that our members need to be based in the UK. ### What would be the starting date? We are keen to grow the team so would prefer if people can start within 3 months of an offer being made and would be happy for successful candidates to start sooner. However, we are looking to recruit multiple people and we are hiring for the long term, so we can support later starting dates if a successful candidate's circumstances require it. ### How many open positions are available? We are looking to grow the team by up to 10 people at Standard level in this recruitment round, with the timing of recruitment dependent on finding the right candidates, project demand and the rate at which we can effectively support new starters in joining the team. ### If my application was unsucessful in the past, am I allowed to apply again this time? If you applied and did not get selected to interview you are welcome to apply again, but we suggest to revise your cover letter and CV and make them more in line with the requirements specified in the job ad. If you applied and were unsuccesful at the interview stage, you are welcome to reapply if your profile has significantly progressed since the interview (new projects, job roles or other sources of experience). In general, we suggest to wait for at least one year before applying again. ## Profile ### Are there particular domains or skillsets that you are interested in for these roles? We are really open to any domain, area of expertise, skillset that is broadly related to research data science or research software engineering. In case you have experience or are interested in High Performance Computing, as part of the open positions we are actively looking for new members of the team to work on that area as Research Computing Engineers (you can drop an email to [Tomas Lazauskas](https://www.turing.ac.uk/people/researchers/tomas-lazauskas) to learn more about it). However, we are recruiting for multiple positions, and are interested in a wide range of skills and experience, so please do apply if you are interested and have experience relevant to research software engineering and data science, broadly defined. You can see the range of [projects the team works on](https://www.turing.ac.uk/research/research-engineering#projects) and backgrounds of [current team members](https://www.turing.ac.uk/research/research-engineering/meet-the-team#the-team) on our website. Some of the team have also been featured in [project](https://livingwithmachines.ac.uk/introducing-david-beavan/) and [institute](https://www.turing.ac.uk/research/research-engineering#researcher-spotlight) spotlight interviews and the [Turing podcast](https://www.turing.ac.uk/news/turing-podcast) (episodes 1, 8, 9, 15, 16). ### What is a Research Computing Engineer? This role presents a new exciting opportunity to join the growing team of research computing experts at The Turing. The team assists the Turing community with designing, building, deploying, and maintaining research infrastructures, as well as providing service delivery and user support for the Institute’s research computing platforms. This is an opportunity for a technically-skilled professional with a good understanding of either cloud or high-performance computing (HPC), experience with both application and infrastructure, and an interest in facilitating and enhancing world-leading data science and AI research. You will build tools and automation to eliminate mundane operations and craft repeatable processes. As part of the team, you will also work with other research software engineers and data scientists on various projects and will participate in all phases of cloud / HPC development, from requirements-capture, definition, and design; to development, deployment, and maintenance; to performance tuning and monitoring. You are also expected to keep up-to-date with the latest cutting-edge developments, and/or make use of the UK’s most advanced computing facilities like ARCHER 2, JADE 2, and Baskerville. ### Should the cover letter address each of the skills and requirements in the person specification section of the job description? It is important that the cover letter, in conjunction with the CV, highlights the requirements listed as "check at Application" on the Person Specification, and shows a (general) understanding of the role of a research data scientist or software engineer. Also, it is important to highlight: - Your past experience working with code and/or data - Why you would like to become part of the Research Engineering Group - How your skillset would complement the activities of the team. ### What does "equivalent professional experience" mean? If you do not have a PhD degree but have worked for a few years and believe you have gained the essential skills mentioned in the Person Specification, we encourage you to apply. Such experience might include exposure to research projects, awareness of the importance of reproducible software and tools, working with or leading a team of people with different skill sets, deciding on how to best achieve an objective from a data science perspective, or presenting advice/results to an audience. We would like to hear about your prior experience in your cover letter. If you prefer, feel free to get in touch with Federico (fnanni@turing.ac.uk) or Camila (crangelsmith@turing.ac.uk) about this point. ### Would not having a PhD prevent me from career progression, in terms of becoming a Senior Research Data Scientist/Research Software Engineer at the Turing Institute? Absolutely not, our internal career progression is based on internal milestones and contributions to the group and the Turing community - do not worry about this at all. ## Position ### What is the difference between a Junior and a Standard role? The main difference between the Junior and Standard roles is that the Junior is a training position for becoming (in 1-2 years) a Standard research data scientist or software engineer. We consider candidates for the Standard position if they have a PhD or an equivalent working experience (meaning for instance a few years of work in a data scientist or software engineer position, in close contact with research projects). If you think you have enough experience you can apply to the Standard position and stress this in your cover letter. You can also apply to the Junior position and mention in your cover letter that you are also considering the Standard position, in case you feel your profile or working experience is in between the two. Feel free to get in touch with Federico (fnanni@turing.ac.uk) or Camila (crangelsmith@turing.ac.uk) about this point. ### For the Junior post it states as Essential: "Fluency in one or more modern programming languages used in research in data science and artificial intelligence". In my case I am familiar with X, would my computing knowledge satisfy this criteria? Depending on the project, we work with different programming languages and different settings, so more than the programming languages you currently know the important aspect for us is the willingness and ability to pick up new languages along the way, based on your previous experience (especially in research and data analysis contexts). ### Given the job profile in the REG team, what do REG members tend to transition to (for eg. Academics, or Industry, or something else)? We have only had a few members of the team who have moved on with their career trajectory so far. Some of them moved to a traditional academic position (for instance pursuing a PhD, taking a professorship), and others to industry and government positions. Others have progressed to more senior roles within the team. ### Would the candidate be able to bring their own projects with them and/or seek funding while in the role ? The team's role at the Turing is to collaborate with institute researchers to support their research agendas, rather than pursuing our own. It is much, much more usual for team members to be working on other people's projects than their own. If you would like to prioritise pursuing your own research agenda at the Turing there are [other types of positions](https://www.turing.ac.uk/work-turing) at the Institute that allow you to do this. However, we are keen to support team members in pursuing their careers, including developing and leading projects. We would generally expect these to be projects that would be a sufficiently good fit for the team and the Turing that we would take them on even if the particular team member was not involved. We would also expect team members that do lead their own projects to continue to work collaboratively with others in REG and the Institute. We would therefore not expect people to be bought out more than 50% on their own project and would expect the project to involve other members of the team or the Institute. Nevertheless, while a permanent role within REG is not meant for pursuing an independent research agenda, we highly encourage initiatives and are open to hear new ideas and contributions. So, if you think your project would benefit our group and the Turing (and vice-versa) you should highlight it in your cover letter. ## Interview ### Could you describe the interview process? As we mentioned in the job description, all interviews are currently held remotely. We operate a two-stage interview process. If you are successful at the screening stage, you will be asked to attend the first interview via video call. In this interview, you will be expected to give a ten-minute presentation on code you have written that either demonstrates an algorithm that you consider important in data science or illustrates your use of good research software engineering practices. Existing examples of work are encouraged as long as the code was substantially written by you. You should be prepared to answer questions about both the code and the research challenge it addresses or the algorithm it demonstrates. Any source code shared for the interview will be treated in the strictest of confidence. The second interview is usually held about one week later for successful candidates. This interview focusses on your previous experience and competencies for the role. There will also be a problem-solving discussion with the interview panel, where you may want to use paper and pen to arrive at an understanding of a proposed data analysis challenge. In both interviews, there will be the opportunity to ask questions about the role and the team. ### Does the algorithm need to be written entirely by me? In the this interview we expect you to demonstrate your ability to write code for addressing a data science task in a reproducible manner and/or to use good software engineering practices for developing research software. This could be done by presenting a data analysis script or piece of research software written entirely by you, or by presenting a larger piece of research software you have made a significant contribution to. Using existing libraries that implement algorithms you are using is fine (and good practice), but in all cases there must be a significant amount of code you have written yourself, it must be clear what code you have written yourself,and we expect you to talk about and answer questions on (i) the code you have written, (ii) the underlying algorithms or methods your code is implementing or using, and (iii) the research domain or question for which you are applying them. ### Can you give any more detail on what the panel expect from the algorithm which will be presented - does it need to meet any certain criteria, for example have a certain level of complexity? In this interview we expect you to demonstrate your ability to write code for addressing a data science task and/or to use good research software engineering practices for ensuring research reproducibility. This could be done by presenting for instance a data analysis script written by you, where you would describe us the implementation and answer some general questions. The adopted algorithms can be ones available in existing software libraries, integrated for intance in a data analysis workflow, but they can also be written by you if you prefer. We will not evaluate you on the complexity of the algorithm, but in your understanding of its implementation, awareness of its potential downfall and its overall appropriateness to solve the problem you are trying to address. ### Will I be only explaining the algorithm / analysis / software, or running it and discussing the results? You will be presenting your chosen analysis, algorithm or software and its implementation, but you should be able to talk about the results of the analysis or the performance of the software. It would be useful to be able to run the code to generate results, but we recognise this is not always reasonable in an interview setting. If this is not not feasible then you should bring some previously generated results so that we can have a conversation around inputs and outputs. The discussion will likely touch on the performance of the algorithm in terms of e.g. speed, accuracy or range of applicability, but our goal is to evaluate your understanding of the algorithm, analysis or software and its implementation rather than to assess you on the performance of your particular implementation. ### Should the presentation be verbal only or should I produce some materials alongside it - would the committee prefer presentation directly from an IDE, or using a presentation software? You should be able to show and (potentially) run the code (this could be using Jupyter notebooks, Rmarkdown, a mix of an IDE and the command line). You are welcome to include any other material that could be useful for supporting your presentation. ### What will the problem solving aspect of the second interview involve? Will I be asked about particular data science or machine learning approaches? Will I need to write code or explain the details of an algorithm's implementation? In this interview we expect you to discuss a question related to the analysis of an example dataset. All information required to answer the question will be given to you in beginning of the interview. **No preparation or specific technical knowledge** is expected or required. The goal of this exercise is not to test your understanding of any particular algorithm or data science method, but rather to assess your general approach to tackling a data science question. This exercise will not require you to write any code or explain the details of any algorithm. The exercise isn't assessed on whether or not you know the "right" answer to a particular data science question, but on the process you go through to understand the question and the data and your reasoning for the approaches you suggest. Taking some time to think through the question is fine, as is asking questions to the interviewers. Consider them as subject matter experts coming to you to answer a question using their data. ### Is there anything in particular I should prepare for the problem solving interview? Nothing at all. The best thing you could do to be prepared is to eat well, take some long walks far away from the computer and get a good night's sleep. ## Offer ### What are the reasons of your job offer? Is the salary negotiable? Fairness and transparency are key values for us and our goal is to avoid inequity by ensuring we are paying people the same salary for the same level of contribution to the team. **Our offers are not negotiable** and set by considering the level and applicability of each candidate’s experience relative to their future peers already working in the team. We have annual pay reviews, where we actively review the performance and development of each team member against the expectations of their position within their seniority band and make salary adjustments where these expectations are being exceeded. Additionally, the team is growing and there will be opportunities for people to progress in their career. Becoming a member of REG will also bring the following benefits: 1. Flexible working: we are currently trialing being a “remote-first” group for the entire decision-making process, but as the office is now fully re-opened members are free to choose how often they come into the office. 2. The opportunity to work and collaborate on cutting-edge data science projects with high-profile institutions, but with the benefits of working in a full-time position (better than a post-doc, better than many data science roles in industry). 3. Strong input into which projects you work on. We value providing the experience of learning something new in a collaborative setting, and prioritise staffing people onto projects they would like to work on rather than maximising the fit to people's prior experience. Our group is made up of people with a wide range of skills and experiences and we generally find we can put together project teams with a suitable mix of skills from those who are enthusiastic about each project. 4. Formal support for professional development via learning and development plans each team member develops with their line manager during the probation process, annual objective setting and regular 121s. These learning plans feed into the projects people are allocated to and the roles and responsibilities they undertake within these. 5. Support to team members with their development outside of projects, providing 20% time that is split evenly between self-directed development and getting involved in one of the group's or institute's service areas. We support team members to attend conferences, workshops and courses, and there are many seminar series and interest groups at the Turing that team members are encouraged to get involved with, including the team’s own weekly Tech Talk series. 6. The opportunity to get involved non-project work through one of the team's service areas, some of which support the running of the team and some of which support the wider research community at the institute (for instance Recruitment, Planning and finance, Training, EDI, Communications, Research computing support, Research programme liaison). Some service areas require siginificant support and will have core team members who have chosen these service areas instead of a full project workload, but all team members have a 10% allocation to volunteer for a service area of their choice. 7. The opportunity to contribute to how the group works and grows. We run the team in an open, consultative and collaborative manner and almost all of our decision making on how we organise ourselves and develop the team is done in the open on our group GitHub with the opportunity for anyone in the team to contribute. ## My question is not answered here Please contact Federico (fnanni@turing.ac.uk) or Camila (crangelsmith@turing.ac.uk) with any additional questions you may have. We also run monthly [drop-in sessions](https://outlook.office365.com/owa/calendar/REGRecruitmentDropinSessions@turing.ac.uk/bookings/) where you can meet some of the current team and ask them questions face to face. These sessions will be hosted by team members who won't be involved in that month's interview process, and questions you ask at these sessions will have no influence on how your application is treated. To be informed of the details for these drop-in sessions, please register for the team's [recruitment newsletter](https://tinyletter.com/turing-reg-recruitment).