Work Placements

Lancaster's Data Science programme has been developed in co-operation with industry to give our students a great start to their data science careers.

Preparation for a career in data science is at the heart of Lancaster’s MSc. We have worked with a wide range of organisations to provide our students with insight and experience of real-world data science. Throughout their course our students have contact with representatives from industry through group projects on live data, company talks and skills workshops.

The majority of our students conclude their studies with a 3-month placement project on-site with an external organisation. The placement projects generally attract a stipend of £3,000 and provide the commercial experience that enables our graduates to stand out from the crowd. Our students have performed placement projects at Unilever, AstraZeneca, Admiral Insurance, Boots, The Co-operative Insurance and many more. Within our programme, we also place emphasis on helping students develop the soft skills that will enable them to make the most of careers opportunities. In addition to our industry seminars, we work closely with students to help them present themselves to potential employers most effectively.

100% of last year's MSc Data Science students rated our placement project process as either Very Good or Excellent.

Dom Clarke - WWL NHS Trust

Former MSc student Dom Clarke shares his mini-diary about his project at Wrightington, Wigan and Leigh NHS Trust.

"Before the placement began, I collected local weather data (from a public source) so that it could be implemented alongside the Trust’s internal data in a model predicting admissions.

"The first week started with an induction into the team and with familiarising myself with the software and how to extract the Trust’s data. As the week progressed, I was introduced to different teams whilst also having the opportunity to go to off-site meetings to learn about current issues. Once I had settled in, I started using R to inspect the data and gain insight. By the end of the week, I was able to produce a basic admission model."

"In the following weeks, I attended more meetings in A&E and I looked further into improving the model, by observing a variety of potential covariates, some that worked, some that did not. I was able to develop a good benchmark admission model for each day and then I aimed for greater detail by looking at hourly admissions. I was able to build on the accuracy of existing models and my models have been handed over to the NHS for further use.

"This is my first real experience of work and everyone has made me feel very welcome, with individuals from different backgrounds available to help whenever necessary. It has been a great decision to choose to work in data science, it has really justified my choice of study. I have had the opportunity to work alone and with teams, where every day, I have new enjoyable challenges and moments to make an impact.

"The whole process was great, everyone at the office welcomed me with open arms and helped me with anything I wanted. I was provided total flexibility to use any methods I desired, it was really MY project. I had occasional meetings to manage progress with my boss but they were only to check up. In the end, the work performed, proved to be successful where improvement was made upon the current models. I was gifted with the reward of seeing the predictions implemented into the QlikView App and also seeing the work I had done presented and discussed in front of executives from trusts around the north-west. I learnt lots from the placement, from presenting my findings to those without domain knowledge to new applications of machine learning methods. Overall, it has been a perfect placement and provided the essential skills for a full-time position within the data science area. In fact, I started as a full-time data scientist today at Peak BI, a data science consultancy startup based in Manchester."

Hwan Lee - AstraZeneca

MSc student Hwan Lee talks about her placement with AstraZeneca.

"In the middle of January, we finally received the placement list that I was anxiously waiting for. The list included various kinds of projects offered by companies of varying size and location. Some projects required the student to be more specialised in computing, while others required a background in statistics.

"The following week, each student submitted a list of projects, ordered by preference, and the companies made their selections. If a student passed the company’s CV screening stage, they were invited for an interview (phone, skype or in person). The following week, the filled projects were removed, and an updated placement list was released. This whole process was repeated on a weekly basis."

"Our MSc Data Science cohort had already attended interview and CV preparation workshops. On top of this, I sought personalised feedback on my CV from Lancaster University career services and from the placement coordinator. To prepare for my interviews, which required a solid understanding of the project, I used online resources to find out more about the company and about similar projects. Additionally, my professors were always there to help me with technical questions.

"After all the support I received from university staff, I felt like there was no way I could not get my dream project. I received offers from two companies and my final project was selected according to the preference list submitted earlier. Afterwards, I was assigned an academic supervisor (an expert in my project’s topic area) and a line manager from the company hosting the project.

"In early June, I started my placement at AstraZeneca in Cambridge. During the first week, I had a meeting with my manager at the company and my academic supervisor to discuss the project in depth and plan the timeline for accomplishing mini-tasks. I had follow-up meetings with my academic supervisor and manager every Friday. At each meeting, I presented and summarised my work during the week. This experience was unique and valuable since it allowed me to learn from experts in academia and industry and taught me how to approach a data science project from a business perspective.

"In the first 3 weeks, I built a deeper understanding of the patient recruitment modelling techniques for multicentre clinical trials and how this model works under various kinds of trial designs. Gaining a deep understanding of the use and impact of my work allowed me to better appreciate the role and responsibilities of data scientists in the pharmaceutical industry. After week 8, my simulation algorithms were mostly written, and sensible results had been obtained. In the last few weeks, under the supervision of my supervisors, I created visualisations of my results and wrote about my modelling methodology in detail.

"The 12 weeks of my placement passed in no time. Nevertheless, it allowed me to learn more data science techniques, work collaboratively with experts in academia and industry, and efficiently manage a data analytics project. In overall, the placement made me easier to be incorporated in the industry as a future data scientist by providing a clear understanding of a data scientist’s role which helped me to confidently answer interview questions for the job application processes."

Sean Sheehan - The Behavioural Insights Team

MSc student Sean Sheehan describes his placement with The Behavioural Insights Team

"The placement was one of the main attractions for doing the Data Science MSc at Lancaster. As I had not worked in a data science related role before, this was a great opportunity to gain experience before applying for permanent roles after the degree. I was interviewed and accepted for my first-choice placement with the Behavioural Insights Team (BIT), a social good company with a behavioural science focus, based in Westminster."

"The project itself was part of a portfolio of work produced for the Care Quality Commission (CQC), focused on predicting which health and social care providers were likely to fail inspections, with the aim of aiding inspections targeting for the CQC. Additionally, the CQC wanted to know whether incorporating online reviews into the modelling would be beneficial, as part of a wider strategy of utilising text data in their decision-making processes.

"The first week of the placement I was introduced to everyone and really made to feel like part of the team, and then planned the project for the months ahead. The first few weeks were focused on gathering and cleaning data for the modelling. Some of the data was available as downloadable files, however, a large portion of the data had to be obtained from third parties subject to Data Sharing Agreements, and a substantial portion, such as the online reviews, had to be scraped directly from websites.

"Once the data had been collected and cleaned, I began modelling. BIT had an initial methodology they preferred me to follow to ensure business objectives were met, however they gave me a broad scope to try different ideas as I saw fit. The data science team at BIT were knowledgeable and always willing to give guidance. I also received advice throughout the placement from Professor David Leslie from Lancaster University. This was useful as he provided different perspectives and suggested approaches to try, and enabling me to fulfil the academic requirements of the final dissertation as well as the project requirements for BIT.

"I was also given the opportunity to attend a number of meetings with the relevant stakeholders in the CQC, including the Chief Statistician and the Head Analyst, which allowed me to ask questions and gain a deeper understanding of the CQ, allowing me to ensure my work was addressing the needs of the CQC.  At the end of the project, I produced a report and presented my results to BIT, in addition to completing my dissertation.

"I learned an enormous amount over course of the project as well as putting into practice and developing the technical skills and knowledge learned on the MSc course, and being able to contribute to a substantial project in this way has increased my confidence in my own ability. I made some very good friends at BIT in addition to having networking opportunities, and the experience has proved to be especially valuable in securing work after completion of the course. "

Ioannis Tsalamanis - Uniper Energy

MSc student Ioannis Tsalamanis shares a mini-diary about his placement with Uniper Energy.

"The opportunity for a Summer placement was one of the things that caught my eye on Lancaster’s syllabus and it convinced me to accept the place that was offered to me. The placement process started a few weeks in the second term and the excitement built up immediately. The first list of available projects was released to us all and was regularly updated thereafter whenever a new project was agreed between the course coordinators and the company. The list was very long, much longer than the number of students in the class, and a significant amount of time was spent reading the descriptions, discussing with colleagues and asking the placement manager for advice. Big, medium and small companies offered very interesting projects, suitable to everyone’s taste and preferences.  Nevertheless, liking one project and actually convincing the company through the interview to select you was a different thing and smart project choices should be made based on the individual skills required. Interviews were held face to face or through phone and Skype. I was offered a place at Uniper UK, one of my top choices, to work on wind farm wake effects, and decided to accept it."

"Once I had accepted Uniper’s offer, a meeting was held between all sides to discuss a rough outline of the project tasks and specifications. The allocated academic supervisor was there to ensure that the goals were feasible for the duration of the placement and relevant to the scope of the course. Officially, my placement started at the beginning of June and the first week was spent to familiarise with the project goals and finalise the project specifications. I got all the help I needed from the company’s supervisor and access to all required data and previous relevant work.

"Having spent almost 6 weeks at the company’s offices in Nottingham, I was convinced that I made the right project decision. It was very interesting to work on renewable energy technologies and modelling the wind wake effects on large wind farms is a very important factor for predicting and optimising the power output of onshore or offshore wind farms. The collaboration with the team was great and I felt as a valuable member of the team.

"Reaching the completion of the placement, the outcome of the project was discussed with the company team and there was an opportunity to understand the role of this outcome to the company’s future plans. Personally, the placement provided a transition from the academic environment to the industrial environment and gave me the opportunity to prepare for the job interviews that came afterwards. Moreover, the fact that there was a short industrial placement after the MSc was highly appreciated by most of the interviewers and offered a great means of describing how my data scientist skills were applied and enhanced.  Most importantly, the placement allowed me to understand the significance of the role of the data scientist in the structure of a company and helped me boost my confidence as a future data scientist."

Michael Thomas - SITA

"SITA provides IT solutions to clients in the Air Transport Industry (ATI) and has several locations across the UK. My placement was in Southern England, about 45 minutes from London by train. SITA’s office is situated on the lush grounds of a former royal residence in Hampshire. Though I had previously worked as a database administrator, this placement was the first time I worked extensively with what most would call “big data”. The opportunity to apply big data tools and techniques in an industrial setting is what initially drew me to Lancaster’s Data Science programme, so I was very pleased when SITA offered me a placement."

"The aim of my placement was to improve SITA’s cybersecurity portfolio using data science. To kick things off, SITA flew me to their headquarters in Geneva, Switzerland so that I could meet with their cybersecurity team face-to-face. After a few group meetings around a whiteboard, we formulated an appropriate research question. I really enjoyed getting to know the team in Geneva, especially on Friday night when we all went out for a beer.

"We decided that I would use unsupervised machine learning to model a given sector of the ATI. To accomplish this, SITA allowed me to query a distributed data lake containing 5 Tb of data. To access the data, I needed to familiarise myself with the Elastic Stack (Elasticsearch, Logstash, Kibana) and needed to learn some basic Scala programming. Once I was comfortable with Elasticsearch queries, I still needed a way to export the data into an analytics environment. To accomplish this, I collaborated extensively with data pipeline engineers to implement a Spark tunnel using Elasticsearch-Hadoop. We communicated primarily through Slack channels and Skype for Business.

"It took about three weeks for everything to come together vis-à-vis data sourcing. It took another two weeks to pre-process the data and one week to do a thorough exploratory data analysis (EDA). Before going on to do more advanced modelling using R and Python, it was imperative that I clarified a few things that I discovered whilst doing EDA. I also needed to fully understand the systems and processes that generated the data. Since SITA is a global enterprise, this meant Skype calls with domain experts around the world. I communicated regularly with colleagues in the UK, Ireland, France, Switzerland, and the USA. If someone did not know how to answer a question of mine, they would usually know who at SITA would know more.

"The last 6 weeks was filled with modelling and writing. I strongly suggest writing your dissertation incrementally as you progress through the data science pipeline. Though my project involved considerable independent research, the SITA team in England was very supportive. The office environment was quite welcoming from Day 1 and people in the office seemed to be consistently interested in how I was getting along at SITA. When I presented my results at the end of the summer, there were about 50 SITA employees in attendance. Overall, I was pleased with my experience at SITA and believe that it helped me to become a more capable data scientist."

James Leith - Sky Betting and Gaming

"My placement was with Sky Betting and Gaming in Leeds where I was tasked with modelling changes in customer behaviour. On my first morning, I was welcomed by my line manager, introduced to the team and given a desk and Macbook. I was on a team of six people, and they were extremely helpful at getting me settled-in and introduced to key members of staff from other teams. It’s always good to get on well with the tech support people!"

"The exact requirements for the project were not defined at this stage, so I spent some time talking to managers from the departments who would ultimately use the model. From them, I was able to determine what they would like and ask them about their understanding of the behaviour of their customers. I produced an initial specification and timetable and agreed them with my manager. As is so often the case, I found I didn’t keep to the timetable, but by producing it I was at least able to recognise when, and by how far, I was behind. I found it useful on a weekly basis to review how I was progressing, and think about what I could do to get back on-track. I had weekly meetings with my manager, and kept in contact with my academic supervisor, so there was no likelihood of getting too far behind.

"The team turned out to be quite a sociable group so there was no shortage of opportunities to explore the pubs of Leeds. My manager treated us to a night at the races, where I displayed my lack of judgement of horses. We had a corporate day where the company provided buses to take us to an airfield where they had set-up a fairground with free food and drink, and a very interesting guest speaker from Wired magazine.

"The environment in which I was worked was a combination of Hadoop and RStudio. Online data is stored in an Informix database before being uploaded to Hadoop overnight. I extracted the data and did most of the preprocessing using SQL in Impala, one of the Cloudera tools for Hadoop. Once the data was ready for building models, it was exported to a CSV file for importing into R.

"In the first few weeks of the project, I spent some time doing a literature search of studies relating to gaming behaviour. I carried out an exploratory data analysis(EDA) to confirm the findings from the literature and produced a presentation which I delivered to the team. The EDA built confidence in the features I was proposing to use, and I went into the modelling phase quite confident that the models would work well. This was where I hit a bit of a setback.

"My first models performed very poorly. I tried a number of changes to the data and saw little improvement. I checked the code producing my training data, and I couldn’t see any problems with it. As time passed and I was making little progress I became concerned about not being able to demonstrate methods and report results, and the impact this would have on my final report. This was where one of the benefits of having an academic supervisor was shown. I spoke to my supervisor, and he helped me come up with a strategy to move forward and gave me some much-needed encouragement.  I also spoke to my colleagues about the issues I was having and they had some very helpful suggestions. I would strongly recommend anyone in a similar position to take advantage of the experience of their colleagues and supervisor to avoid losing sleep.

"Eventually my results improved and I had enough material for a decent report. Knowing that I was going to have to write a report in pdf, I had used one of my previous assignments to teach myself LaTeX which was time well-spent. I was even lucky enough to find a blog on  www.sharelatex.com  on the subject of laying out a dissertation style report using a master document. Despite being occasionally infuriating,  I love LaTeX. Laying out plots and figures is really easy, and the final report looks great. You just need to remember to compile and save it frequently. I was pretty happy with the final report and the viva went well as far as I can tell.

"To sum up, my placement, while being a lot of fun, also had its challenging moments, which is how it should be considering it’s a Masters programme. I learned a lot from my colleagues, and I learned a lot about how Data Science is done in the commercial world. Sky Bet were great to work with, and Leeds is a wonderful city."

If you would like to apply for one of our Data Science Masters degrees, you need to use Lancaster University's My Applications website.