Dylan Walter Jones

What motivated you to apply for the Social Data Institute internship scheme?
Throughout my studies at UCL, every year my favourite modules were, without fail, the data science modules I got to do through Q-step. I knew ultimately that was the kind of work I wanted to be doing for a living so naturally applying for the Social Data Institute internship scheme seemed like a great step toward my goal. The variety of schemes available was also a huge factor as I felt It would allow me to explore the industry more widely.

Can you tell us about your experience with the internship scheme?
My internship was with Islington Council where I was placed in the People Friendly Streets Team. Their focus was on evaluating the impact of Low Traffic Neighbourhoods, which was a policy to close off roads in residential areas so that cars wouldn’t drive through and cause pollution or higher risk of pedestrian injury. Which, for me, was fantastic, I always loved studying controversial government policies and making life slightly difficult for drivers in London is about as controversial as local politics gets. I had the pleasure of getting to collate all the data the council had collected from the scheme in its first year. I was tasked with simplifying the results down into a single report, and I got to spend hours researching the most common complaints people had about the scheme to see if they were visible in the data. It was an invaluable opportunity to build up my ability to present statistics to a non-statistical audience and to consider the impact of my work in a policy setting. Ultimately it was the major inspiration for me to apply to the government statistical service fast stream where I’m working today. 

What skills did you gain from the internship that you have found particularly valuable for your current career path?
The main skill I got from the internship was the ability to present statistics to a non-statistical audience; learning how to build a narrative with numbers, but also knowing what stakeholders want from the statistics you provide. Analysts are usually caught up with the technical details but when you’re working with policy officials they just need a number that can be understood and will calm the angry emails they’re receiving from constituents. Learning to see the big picture and getting out of the headspace of an analyst is one of the best skills you can have working in data science and is one of the greatest assets afforded me by the Social Data Internship Scheme.

Can you share a memorable project or task you worked on during your internship?
One really cool task I got to work on was with an adjacent team where they were developing a cycleway (which is kind of like a series of connected cycle lanes that form a highway through the borough) and they wanted to see how the cycleway was affecting traffic accidents involving cyclists. So what I did was I created a hotspot map (looks like those rain forecasts you might see on TV) using data from the Department of Transport which allowed the council to see how the geographic location of accidents had shifted away around the cycleway since it had been introduced. It was a very cool visual and it was a great opportunity to put the skills I learned from Q-step into practice.

 How did the skills and experiences from your internship prepare you for your current job?
In my current job, I am constantly sourcing, analysing, and presenting statistics on a wide array of topics about the UK economy and its trading partners. The internship first helped me understand how government bodies produce statistics and the kind of limiting factors they run into. Ultimately all stats are flawed in certain ways but that doesn’t render them useless and my job is to understand what you can and cannot say with a dataset. Experiencing this at Islington Council was my first real-world handling of data issues, and instilled in me the idea that outlining the caveats and the limits of the statistics you have is the most important step in your analysis. The second aspect of that is learning how to communicate those limits/caveats while not letting them drown out what the stats are saying, this is crucial in my current role where I am balancing the need to have concise lines about statistics while making sure those caveats are well represented.

 What advice would you give to current students who are considering applying for internships?
I understand the pressure to find an internship and to get any kind of work experience, but don’t apply outside the industry you want to work in (I wasted so much time applying to pure finance and consulting roles I had no interest in working in). Instead, try to apply as broadly within that industry as possible. I knew I wanted to work with data science but had only half considered doing so in the public sector and it wasn’t till the Social Data Institute presented me with that option that I thought “You know what, that actually sounds really fun.” 

What are your best memories from your time at UCL?
In the absolute busiest periods of exam season, I would always study in the main library in Portico and to get a seat I needed to be there pretty early in the morning. So to motivate myself I would go to the Print Room cafe when it opened at 8 am to get a Danish and an Americano. No matter how stressful the day ahead was gonna be, however many thousands of words I needed to write, those 20 minutes in that cafe were golden.

Mundane exam season moments aside. I had an amazing time working with Islington as part of the Social Data Institute internship scheme during the summer of my second year. Evaluating the People friendly streets scheme was an endlessly entertaining area to work in, it was a great introduction to the working world and I still look back fondly on all the great work I got to do while I was there. It was a massive inspiration for my dissertation which heavily focused on the dynamics of council-level politics and the insights I gained gave my research a tremendous headstart.