What traits do you look for when hiring a data scientist and what abilities make a data scientist successful?
No one thing in particular draws me in to one data scientist over another. A big problem with the data science industry is that it’s so ill-defined, people look for someone who can do everything, which is always a very difficult thing to do and if you do hire those people then they don’t stick around long because someone comes and offers them more money elsewhere.
I tend to look at my team as a whole and try to understand what’s missing. Does this person think in the same way that we currently think? If the answer is yes, then typically I’ll be thinking we need someone who thinks differently because the purpose of my team is to be flexible to clients’ needs and problems. If we’re in those situations and we all think the same way, then we’ll all come up with the same solution or no solutions at all. That’s how I tend to think about my hiring strategy because if I hire all the same people then I will very quickly find myself not being able to be agile and nimble with my team.
Some of my data scientists will challenge me because I’m not considering their way of thinking so it’s useful to have people who don’t look at things in the same way as you. And, the added benefit to that is that you hire very diversely because you’re not looking for a candidate based solely on their qualifications. You’re looking for someone with a very different background and then you get very different types of candidates across ethnicities, genders, etc. and it breeds a really positive team environment.
All of that typically comes from having a good understanding of what my team can set out to achieve, which is relatively difficult in data science because quite often, data science teams in businesses have just appeared. A lot of companies have historically set up data science teams without a full understanding of what they want them to achieve. So, for me the critical thing to do is to identify areas of weaknesses and hire for those weaknesses. For instance, it might be that you’re too top heavy and need someone to come in and do more of the groundwork, or it could be that you’re missing a particular skillset.
What do you think needs to change to help increase the number of data scientists moving into industry?
People need to think about data science in a very different way than how they do currently. I feel the issue that fundamentally exists around bringing more people in to this as an industry, is that it’s so immature because it hasn’t really been a job for a very long time, so the understandings of what that job involves and its purpose are very misrepresented by the people who are hiring and also by the people who are coming out of university who are looking at the job on the market.
I’m finding that a lot of data scientists I interview talk about wanting to build and tune models, and I think that attitude comes from the fact that that’s what they see online when they’re watching videos about data science. And, to be honest with you, once they get in, they’ll realise that that’s actually a really boring job and companies don’t necessarily want that.
A way to encourage more people in to data science is to redefine the discipline and that’s very hard to do, but I think there needs to be as I don’t think this catch-all term of data scientist can stick around for much longer. We’re already seeing a lot of newer job titles such as machine learning engineers and data engineers. You’ve also got to reclassify what a data scientist actually is to help people from different disciplines apply for these kinds of roles.
What is the biggest impact Covid-19 has had on your line of work?
In the industry of market research, it’s been going through a period of transition for a long time. That transition is more towards newer types of data and techniques, thinking about different ways of supporting clients and Covid-19 has accelerated that.
In terms of how much work we must do, it’s still lots but in terms of the type of work we do it hasn’t really changed. The way we work has fundamentally shifted – it’s obvious we’re all working from home and we’re having to be a lot more joined up. I have a daily stand up with my team, which I’ve found force those more personal moments by making space for people to say stupid stuff, talk about their weekends and discuss the fact that they’ve got ridiculous hair – all of those kinds of things are really important to make sure that you don’t get as disconnected.
I think in general Covid-19 hasn’t impacted data science as a function because so much of our work is remote and relies on technology that’s in the cloud, etc. so Covid-19 hasn’t had a huge impact on our industry specifically, outside of the fact that businesses are starting to struggle more. I think there’s just a repositioning of resource into differently impacted industries because of Covid-19.