The data series: Interview with a Data Scientist at Preqin

Article written by Jacob Knight, Head of Data of X4 Technology.

Through the use of algorithms, data mining, artificial intelligence, machine learning and statistical tools, data scientists crunch through millions of data patterns that could not be untangled by the human eye.

In the current climate, where businesses have had to adapt and continue to adapt to survive, hiring a data scientist can give you an instant competitive edge. They play an incredibly valuable role to any business and add value to any sector. I spoke to Sam Parsons, Data Scientist at Preqin to hear about his journey into data and his thoughts on the future of data science.

What interested you to start a career in data science?

I’ve always been interested in explaining complex patterns, and this was a strong motivation for my degree in statistics. I found working in this area in academia very satisfying, but after a while I was keen to move into industry. After I made the move, data science was the obvious choice for a career that would have the same intellectual challenges as academia but in a business setting.

What do you enjoy most about being a data scientist?

Data science is a mix of theory and practical problem solving, I really like that it has to make sense and it also has to work. When you make predictions and they have a high accuracy, that’s very satisfying. I also enjoy coding up models and implementing them. You need to understand what you’re trying to do, why you’re doing it, and have a detailed plan of how you’re going to do it. The how part is key and brings the ‘what and why’ parts together. It’s a great feeling when you implement a complex model and it works the way it’s supposed to.

What real challenges do you face as a data scientist?

I think that the initial data collection for a project is probably the most challenging part of my work. In the early stages of a project the problem specification can be a bit vague, and exactly what data you need can be unclear. This is especially challenging when the problem you’re trying to solve is not suited to the structure of the database the data is held in. It can also be a challenge to balance your time between using a pre-existing solution and doing R&D to build something new.

What excites you most about recent developments and the future of data science?

I think recent advances in NLP are very exciting. GPT3 is incredible and shows that sky is the limit for using machines to solve language-based problems. More generally, the network architectures that have been published in recent times are really powerful and have a lot of potential for further development. It’s also exciting to see the larger data science eco-system grow and mature. There are many packages, frameworks and services available now that allow you develop and customise models quicker than ever before. This process feeds on itself and has a snowballing effect, and it’s fascinating to wonder how much more sophisticated our tools will be in a few years’ time.

Any words of wisdom to other aspiring data scientists just starting out?

Have a plan for the early years of your career, particularly in terms of any education you invest in for yourself. Entry level positions are different, depending on your educational background, and you need to decide when to stop learning and start gaining practical industrial experience. There’s not one right answer but making the right decisions for you can have a big effect.

If, for example, you really enjoy the theory component of data science then a PhD will probably be enjoyable for you, and it has strong long-term career payoffs. But while you’re studying there will be others who are advancing in the workplace, and if you’re more suited to learning from practical experience then your time might be better spent by learning less and getting your first position sooner.

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