My Placement Experience: Lessons and Triumphs

post by Kuzi Makokoro (2022 cohort)

Reflecting upon my placement, a key lesson around the most important decision to make before starting a placement, was to consider the specific skills and experiences I hoped to gain. This past summer, I had the opportunity to partake in a placement with my industry partner, Co-op, which turned out to be a remarkable and invaluable experience for my professional and academic growth.

Before finalising the arrangements for the placement, including setting the dates, duration, and defining the project, a series of discussions took place between my supervisors and me. We assessed the multitude of opportunities that this placement could offer. It was during these deliberations that the versatility of a placement’s benefits became apparent to me. One option is to align the placement activities with your ongoing PhD research, ensuring that the work is not only relevant to your academic pursuits but also meets the strategic needs of the industry partner. This synergy often results in a mutually beneficial outcome that propels your research forward. Another approach could be to take a break from academic work to gain a breadth of experience in the industry, thereby expanding your professional network and engaging in projects that are also of interest to you.

Having spent the last nine years in commercial roles within various industries and capacities, I was already familiar with the dynamics of industry life. This pre-existing industry experience informed my decision to select a project that complemented my PhD research. Once I made this strategic choice, the focus shifted to pinpointing a suitable project. After numerous consultations, we collectively decided to concentrate on the Healthy Start Scheme—a government- initiative designed to aid low-income families with children under four by providing them with essential foods like milk, fruits, and vegetables. This project was not only crucial to my industry partner but also resonated personally with me, as it underscored the meaningful impact of data-driven initiatives on societal well-being.

The research objectives for the placement were ambitious: to utilise predictive analytics to predict the uptake percentage of the Healthy Start Scheme using food insecurity measures and to apply machine learning techniques to identify and understand the factors that influence uptake significantly. Working in conjunction with an industry partner meant that the practical application of my research findings could potentially aid the partner in supporting and promoting the scheme more effectively.

Entering the placement, I had certain preconceptions about how the experience would unfold, the nature of the work I would engage in, and the interactions I would have with various stakeholders. However, the practical aspects of my placement differed from my initial expectations. I quickly realised that my chosen topic necessitated a more independent working style, with periodic contributions from my industry partner rather than continuous collaboration. This shift led me to a new understanding of the role of a researcher in a consultative capacity, working in partnership with an industry entity. The experience also allowed me to lead a research project autonomously and understand the nuances of impact work. My responsibilities included initiating regular meetings with my contact at Co-op, seeking input and assistance from the wider team when needed, and managing the project’s pace and milestones.

In hindsight, although the timing of the placement originally seemed appropriate, I later reflected on whether doing it later on in my PhD program might have allowed for a richer output. The project demanded proficiency in skills that I had not yet mastered at the time, necessitating a steep and rapid learning process. This included developing an understanding of predictive analytics methodologies, acquiring proficiency in programming languages such as Python, learning about digital data collection techniques, and interpreting complex model results.

Consequently, what was initially set out to be a three-month placement evolved into a five-month project, as additional time was required for me to learn, adapt, and then effectively engage in the research. I adopted various learning strategies, such as the accelerated learning techniques outlined in Jake Knapp’s book “Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days,” which aided me in assimilating new information rapidly, trialling different approaches, and breaking down the project into smaller, more manageable tasks. Ultimately, I was able to enhance my skill set and produce actionable insights from the project, though a better approach to defining deliverables within the given timeframe would have been advantageous.

The research outcome was insightful; we identified several strong predictors within the model, such as income deprivation and language proficiency, as well as intriguing variables like household spending on fish and the caloric density of purchases. We explored various ways in which my industry partner could leverage these insights to better support the Healthy Start Scheme in communities where it is most needed.

In summary, the placement was a journey of adapting to a different work environment, setting pragmatic goals, and scaling up professionally. This learning experience has been instrumental in advancing my PhD work. It reiterates my initial emphasis on the importance of understanding what you seek to achieve from a placement. Although I had not initially set out to acquire these specific skills through the project, they have proven to be of great value as I continue with my PhD journey. Looking ahead, I am excited about the prospect of converting this project into my first published academic paper.

Reflections on my placement at the Department for Transport

post by Phuong Anh (Violet) Nguyen (2022 cohort)

I began my placement in the Data Science team of the Analytic Directorate at the Department of Transport in April 2023 to gain access to datasets for my pilot research. However, I feel that my internship officially began in July, when I was able to become familiar with my work and knew precisely what was expected of me. I still remember the rainy afternoon when I went to the warehouse to collect my IT kit. It was quite a funny memory, and now it is quite emotional to pack and return my kit as my internship is over.

My project

My internship was an integral component of my doctoral research on “Using personas in transport policymaking.” I aimed to combine various data sources to investigate the travel behaviour of various demographic groups, and then use this understanding to inform transport planning and policy formulation.

I began by examining multiple DfT data sources, including the National Transport Survey, Telecoms Data, and Transport Data dashboard. I arranged meetings with several data team members to ask them about how they analysed these data in previous projects. I also had opportunities to discuss with members of other teams including System Thinking, Policy, Social Research, and Data administration… to learn about their work and the policy-making process at DfT.

Since the official release of the transport user personas report in July, I have collaborated closely with the personas team. I began with an examination of the methodology for developing personas. I also attempted to apply additional data science techniques to the same dataset (National Transport Survey) to cluster travellers into distinct groups and compare the results of the various methods.

DfT published transport user personas. (https://www.gov.uk/guidance/transport-user-personas-understanding-different-users-and-their-needs).

I worked with the Social Research team to organise workshops introducing the potential of using personas in DfT’s work, such as Road Investment, AI Strategy, and Highway… In addition, as part of my research, I utilised the Social-technical framework theory to structure the transport system and then gathered data to present and analyse the interaction between personas and other transport system components. On the other hand, I learned how policy is formulated and I will continue to work with the Policy team to investigate how personas can support their work.

Some lessons for myself

About work

Working on the Data Science team, which is part of the Advanced Analytics Directorate, was an excellent opportunity for me to improve my statistics, mathematics, and programming skills. Colleagues were very knowledgeable and supportive. Through the team’s regular meetings and project summaries, I got a general understanding of which projects are active and which models and methodologies are used to solve the problems. Sometimes I found myself bewildered by mathematical formulas and technical models. Although I have studied Data Science in the past, which has provided me with a foundation in Data analysis and Programming, in real work data looked more complicated. The assignments in the placement have taught me how to overcome the challenges of dealing with multiple data types.

Working in Civil Service, I had access to numerous training resources, workshops, and presentations, including but not limited to Science and Programming, this course covered user-centric services, artificial intelligence, evidence-based policymaking, and management skills. This is why I regard my civil servant account to be so valuable.

I received numerous perspectives and comments on my research proposal thanks to weekly discussions with my line manager and multiple team members. Importantly, I learned how to present and explain my ideas and academic theories to people from diverse backgrounds, as well as how to make the ideas clear and simple to comprehend. I believe this is a crucial skill in multidisciplinary research, communication, and public engagement.

About working environment

This was also my first time working in civil service, a “very British” working environment. Even though I have more than five years of experience in the airline industry, it took me a while to become accustomed to office work. It could be because the working environment in Vietnam differs from that in the United Kingdom, business differs from civil service, and full-time office work differs from hybrid work.

In addition to the knowledge and expertise I gained, I learned a great deal about time management and how to use Outlook professionally to organise my work, as well as how to spend concentration time between multiple meetings every day. This was extremely helpful when having to divide my time between multiple tasks, such as PhD research, placement, and meetings with supervisors from various institutions. In addition, I believe that working in person in the office is more beneficial than remote working, having access to a larger screen. being able to meet and discuss with multiple people, rather than being limited to 30-minute meetings and a small laptop screen. Thus, I travelled to London every week. These regular catchups with my line manager/industry partner proved helpful because the industry partner was able to provide a realistic perspective and I was able to update them on my work and interact with other DfT employees who supported my research.

About my personal development

Since I began my PhD journey, I have experienced many “first-time experiences”, including my first time working in Civil Service. This placement is not only an integral part of my PhD research, but also provides me with a great deal of experience and lessons for my personal growth. It was so unusual and sometimes difficult, but it forced me to leave my comfort zone. I was confident in my ability to perform well, having had the previous experience of being an airline strategist in the past, but the new experience of being an intern, learning something new, made me humble and enthusiastic as if I had just started school.

My principal lesson is to simply DO IT, JUST DO IT. I believe that the majority of my depression stems from my tendency to overthink. There were times when I examined the data set and had no idea what to do. I was even afraid to send emails or speak with others. However, when I actually did what I should do – WRITE something and ASK some questions – and I saw results, I realised that that work is simpler than I originally thought. Then I learned that sitting in a state of distress and worrying about the future is ineffective in resolving the issue. I must take action and be diligent to see myself become a little bit better and better every day.

The summer was very short, and most colleagues took vacation time. Honestly, the internship was not “comfortable” in the beginning, but now I believe everything is going well. This placement is also assisting me in developing a clearer plan for my PhD project. I am grateful for the support and lessons I have gained from this opportunity, and I am considering another summer placement next year.

My Internship at Capital One

post by Edwina Borteley Abam (2019 cohort)

My internship at Capital One started mid-November 2021 and ended mid-May 2022.  Capital One is a credit card company originally situated in the US with two branches located within the UK in Nottingham and London specifically. I interned at the Nottingham branch over a period of 6 months, on a part-time basis.

The company has several departments and units. I was placed within the Data Science team which forms part of the wider Data group within the organisation. There are three main sections within the Data Science team namely Acquisition, Customer Management and the Bureau team. The Acquisition team concentrates on building models to score new credit card applicants. The Customer Management team focuses on managing and monitoring the behaviour of all existing credit card customers and credit line extensions and the Bureau team manages all data and information exchanged between credit bureaus and Capital One.  For my daily work, I was placed within the Customer Management team and collaborated on two related projects- (Onescore2 and Challenger Model).

The internship:

Onescore2 project involved creating machine learning models to manage the behaviour of existing credit card customers. I worked together with my manager to build models to predict customers likely to default on their cards over a defined period of time.  We used R (a statistical programming software) as the main tool for the project. The specific activities assigned to me on the project involved creating the R program files for executing the models, monitoring the progress of the models’ execution, collecting and interpreting model results, and updating the GitHub repository with project outputs. The previous knowledge and skills acquired from the Data Modelling and Analysis course in year two of my PhD helped me understand the technical details involved in the analysis and to carry out my assigned duties effectively on the project.

The second part of this project is the Challenger Model project and it involved building different models in Python to compare their performance with Onescore2.  The project was an exploratory study of different conventional models in predicting the likelihood of default. The Challenger Model project serves as a baseline to compare with results from my PhD work, which potentially could form part of my PhD thesis. As this phase of the project is linked to my PhD work, I benefitted from the guidance and input of my supervisor.  While working on the Challenger Models, I held periodic meetings with my manager, supervisors and other members of the Data Science team where I presented on progress and discussed possible directions for the project.  I also took part in weekly stand-up sessions where all associates within the Data Science team shared updates on ongoing projects.

My reflections:

Looking back on my internship, overall the experience has been insightful, an exciting journey and a time of personal development.  I have grown and evolved in several areas in terms of interpersonal and professional skills.

Upon arrival in the first week of the internship, my manager was deliberate to arrange informal meetings and chat sessions with other members of the Data science team.  These introductions and chats exposed me to a range of people in various roles and at different levels of leadership in the team. It helped to quickly integrate into the team to create new connections and meet new people. Despite being naturally reserved, I enjoyed the conversations much as everyone was friendly. I was encouraged to step out of my shell to interact with more people. During my interactions, I seized the opportunity to ask all the lingering questions I had on the topic of credit scoring which is also at the heart of my PhD research. Each person was friendly and particularly eager to answer all my questions and chat about the work they do.

Apart from the Data Science team, I had the chance to speak with other associates in other departments of the company and that experience was reassuring and enhanced my confidence at the workplace. I got first-hand experience in mixing with different people from different backgrounds in an office setting and learning to blend with them.   The conversations in the first couple of weeks opened up my understanding more on the details of credit scoring and credit cards. I got more understanding of how the different teams work together to make credit cards available to people and how customers are managed and credit lines extended. I had the opportunity to join major meetings and to hear updates on projects being worked on within the different departments of the organisation. This also gave me a wider view of other aspects of the business.    I was able to connect how the theory of credit scoring I had read in books and research articles played out practically in the real world through this experience.

During my internship, I worked both from home and the office.  Every week during the first few months, I worked three days at home and two days in the office.  I found commuting to work on time a discipline to develop as this was my very first time working outside of industry. Although challenging initially but got easier with time.  The regular catch-ups and progress updates with managers and my supervisor were sometimes strenuous and nerve-wracking, however, it trained my communication and presentation skills.

The work culture in Capital One challenges associates to give their best on the job but at the same time encourages relaxation and places such high priority on wellbeing.  Unlike other work environments, I was surprised to find several fitness and relaxation points like the gym, tennis and pool table strategically placed in the Capital One building to support associates. In addition, during my internship, the company observed a day of fun activities for its associates every quarter of the year just to have a break from work.  This shaped my perceptions about the working environment.

Capital One is the industry partner for my PhD and I was privileged to have access to their data for my PhD work. Through my connections with the team members, I was able to easily recruit participants for my first PhD study which I believe would have been difficult otherwise without the internship.  Overall, I enjoyed the internship and the experience has been beneficial not only for my PhD but for my personal development.