Shaun Gupta has a MSci in Physics from UCL and a PhD in Particle Physics from Oxford. He tells us how he started his career in Data Science and what being a Data Scientist is like.
I am currently employed as a Data Scientist at a startup called Row Analytics. Data Science is an emerging field, and it involves using a mixture of coding and statistical analysis to answer questions using big datasets. The company is very small (less than 10 people), which means my role actually covers a wide range of different activities in addition to just Data Science. It is an exciting place to work as I am helping to build the company from the ground up, in a sector that is still relatively new and constantly evolving.
How did you move from a PhD to your current role?
After undertaking an MSci in Physics at UCL, I pursued a PhD in Particle Physics at the University of Oxford. During my final year, I spent a month taking part in the 2015 Science to Data Science (S2DS) bootcamp, based in London. The school was a pivotal opportunity to learn more about the emerging field of Data Science, and showed me how relevant my skill set was in industry. As part of the school, I spent time working on an exciting project with my current employer Row Analytics, who offered me a full-time position once the school was over.
What does an average working day look like?
As the company is currently very small, I tend to perform a variety of tasks as part of my job. My time at the moment is split between helping to set up an infrastructure in the cloud on AWS, setting up and configuration databases (noSQL and graph based), building a web application (both front and server backend), writing programs to scrape unstructured data, and performing Natural Language Processing (NLP) on the data.
How does your PhD help you in your job?
In essence, my role is very similar to what I did during my PhD, only using data from a different source. As a result many of the techniques and practices I learnt during my PhD are useful in doing my job. These include programming, problem solving skills, strong mathematical skills, statistical analysis techniques including knowledge of learning algorithms, and the ability to work independently in a research driven way to develop new ideas/products.
What are the best things about your job?
I enjoy working in a constantly evolving field with many opportunities to get involved in new projects and learn about new cutting edge techniques. I also find it exciting working for a company at such an early stage in its development, and being involved in shaping its future. I am also lucky in how flexible my work is, with the ability to work from home a couple of days a week.
What are the downsides?
As the role involves a lot of coding, a lot of time can be spent fixing bugs. Also a lot more time is spent working with the data and structuring it in the correct way for analysis than one may expect initially.
What tips would you give researchers wanting to move into the same, or similar, role?
Data Science is a new and exciting sector that is rapidly growing, and so now is the perfect time to get involved. I would say solid programming skills coupled with good analytical ability is key. Therefore, I would advise to brush up on your coding skills in languages such as Python and R, and your knowledge of statistics. Attempting challenges on sites such as Kaggle is a useful way to do this. Attending a school such as S2DS will help you to learn more about the industry, and get involved in real world applications of Data Science with companies. There are also many meet-ups around London and boot-camps that are worth attending.