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It’s that time of year when there is a lot of newly graduated talent coming onto the jobs market, it’s a good time to talk about the importance of being honest with each other when advertising jobs.
A trend I’ve spotted increasingly over the last few years is a conflation of different data-driven jobs on the market. At graduate level, you might be really excited at the prospect of getting to work on a highly technical project, to really get your hands dirty with some complex tasks. Some employers, both knowingly and unknowingly leverage this enthusiasm as they know that a job advertisement will get much more traction if it is implied that it is a more glamorous role. This tactic isn’t an occasional thing either, it’s rampant.
For example, there are a lot of jobs currently being advertised as Data Science or Machine Learning roles because those are perceived as more attractive job titles that will attract more applicants. The descriptions of these jobs include a little lip service to the role alluded to, but when you dig down, quite often the company doesn’t have the data, the infrastructure or the company buy-in to carry out advanced analytics projects. What they actually want is someone to work on descriptive reports, maybe providing some insights to the business.
Applicants: Beware of, “Do this work you’re not really interested in, but which the company needs now. We’ll get to the interesting projects you want to work on at some non-specific point down the line''.
Employers: Resist the urge to oversell the position you’re trying to fill. We all want a long-lasting, harmonious working relationship and this can’t happen if the employer and employee are working with totally different expectations.
Applicants: The ability to do part of a job description doesn’t necessarily qualify you to do the others. If you’re a Big Data Platform Architect, you are no doubt incredibly talented, but an Insights Analyst would likely be a better hire if a report on market trends is needed.
Think long-term. Be wary of taking a job for, “the experience”, when you really want a different role. The first job may not move you any closer to it. For example, because they have similar names, and they are connected or often work in tandem with each other, people think that a year of experience as a Data Analyst makes them more employable as a Data Scientist when often all it really does is make them more employable as a more senior Data Analyst. It’s the equivalent of getting a year of experience working on a wheat farm to make you better at baking bread. It’s not entirely useless, but there are better ways to get to where you want to be.
Instead of applying for jobs that kind of sound like what you do, know what you want.
Differing answers to the above questions will lead to very different jobs.
Employers: When deciding how to advertise your available job, you really need to be able to answer some basic questions about what will be involved on a day to day basis. Build a check-list around the things that are of critical importance, for example:
Finally, a note for employers hiring Data Analysts and Data Scientists.
Know what you need from employees and be open with where the role will lead. If you’re not in a position to work on advanced analytics projects at the moment, hire what you need NOW, not what you might need someday. You’ll end up with an employee more capable of doing what you need, and they’re going to be much more interested and engaged with the work. This leads to lower staff churn, and better morale all round.
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