Because what you don’t want in your next role is just as important

One thing we seem to forget when we are looking for our next role is that the job interview is for the candidate as much as it is for the employer. We tend to be so preoccupied with acing that interview that we fail to recognise potential red flags; we may then end up in a role that is not what we expected it to be, prompting us to fall out with our jobs really quickly. While this is generally true for every job, it is particularly so for Data Scientists.
Research shows that Data Scientists don’t stay long in their jobs, with the average length of stay in an analytics/Data Science job being 1–2 years. The gap between expectations and reality appears to be one of the main drivers of such high turnover: we are promised the sexiest job of the 21th century but, more often than not, we end up churning reports in Excel or working on models that will never see the light of (production) day.
If we are frustrated at our current role, quite understandably, we might want to move on and find a better one. But how do we make sure our next role is a good match and we are not walking into more of the same?
As candidates, we should actively look out for signals during the interview process. But, while it is fairly easy to spot the plus sides, it is not as easy to identify the red flags that should raise an alarm and prompt further investigation. So, here are 5 common red flags Data Scientists should look out for.
1. The job description is vague and too general.
Just like you are selected based on your CV, you should start the selection process at the job description. If it is too vague, lists unrealistic requirements (for example pretty much every existing or defunct programming language), uses buzzwords such as Big Data or AI without a real reason, don’t bother. Chances are someone decided they needed to get a Data Scientist (because that’s what the cool companies do these days) but they don’t have a clue about which skills they are looking for in a Data Scientist or why they even need one. You might want to dig deep into what type of challenges you will face or projects you will work on and try to get them to be specific. If they can’t, there’s a high risk you won’t get to do any Data Science at all.
2. The job interview is too easy.
If the interviewer doesn’t probe you trying to find out how deep is your knowledge of the topic and lets you off too easily, either you are interviewing for a role below your level or they don’t have enough experience themselves. In both cases you might want to walk away. In the first case the job might not be challenging enough and you’ll find yourself bored soon. In the second case, assuming the interviewer is more senior than you are, you might want them to be more experienced in order to learn from them and grow as a scientist. Ask them to describe their typical work day or ask them to talk you through past projects and current challenges to get a sense of what you could realistically do there.
3. They claim to be data driven.
Most companies claim to be data driven but very few are. Sure, the analytics function will be data driven (hopefully) but are the other departments you’ll work with? Luckily, it’s very easy to find out during the interview. Ask them for specific examples of how they used data to make decisions. If they can’t provide any, you’ll know it’s not true. So, unless constantly having to explain to people the value of your work is something you enjoy doing, you might want to pass.
4. They are undergoing a data migration.
Now, this is not bad per se (quite the opposite, actually) but it means that, if you join in the midst of a migration, you will be likely stuck working with legacy systems for a period that can range from a few months to years, depending on the status of the migration. Data issues will be frustratingly common and automating anything you produce will be pretty much impossible as most things will be put on hold until the end of the migration. It’s a good idea to inquire on the progress of the migration during the interview: ask when it started and how many more months it’s expected to take (and also be aware that these things tend to take a lot longer than expected). If they’ve just started and you can’t wait to get on the cloud, it’s probably better to look somewhere else.
5. They want you to generate ”insight”.
As a Data Scientist you should want to work on projects that have a clear business goal, a well defined deliverable and a measurable impact. These are the things your next employer will be looking for in your CV. Insight, however, doesn’t have any of these qualities. Often there is not a clear goal behind the request but a vague ‘nice to know’ and, if it’s not scoped properly, the risk of being stuck in a never ending loop of ‘it would be interesting to look at X’ is very high. Also, and more importantly, it is very hard to quantify the impact of insight. It will exist in some director’s head and may or may not influence their future decisions. If this is all you’re being asked to do, chances are you are going to be an overpaid analyst rather than a Data Scientist.
By themselves these flags aren’t necessarily bad but they should definitely sound an alarm. However, just because you have identified a red flag, it doesn’t mean that job is not for you: every role has pros and cons and the positive aspects might surpass the negative ones. So, ask your questions, collect your data and, like the good Data Scientist you are, draw your conclusions and make your decision. There is no such thing as the perfect job, only what is right for you and your aspirations at a given point in your career.
In conclusion, remember that the Job Interview is your opportunity to find out about your potential new job so make the most of it. Don’t be afraid of asking the hard questions; no one is going to judge you badly for that but a bit of research during the interview (or even before, to be fair) can save you a lot of misery down the line.
For tips on how to make your CV stand out, instead, check my other article.