Welcome to the final part of our four-part series on choosing your next data science or analytics role. We have covered a lot of ground so far: from choosing the right location and time in Part One, the right industry and company in Part Two, to thinking about the people you will work with in Part Three. If you have made it this far, congratulations! In this final installment, we will focus on the role itself. What part will you play in the organisation? Where will you fit in?
A good place to start is to understanding the history of the role. Does this role already exist in the organisation? What is expected of this role? Is there someone else in a similar role you can talk to in order to learn more? Alternatively, if is a new position, try to understand why it has been created. Is it to address an emerging need that did not exist before, or an established need that had never been adequately covered? Find out which stakeholders have been pushing for this role, as buy-in from senior leadership coupled with a clear mandate will make your life a lot easier once you join.
Next, dig into what your personal responsibilities will be. It is helpful to think about both the short term expectations and long term opportunities. While in some roles you will have the luxury of slowly ramping up, in others you will be expected to hit the ground running. Will you have to carve out your role yourself, or will you be fed well-formed tasks? While as a data scientist you may prefer to have problems come to you clearly scoped, realise this might also mean less degrees of freedom and therefore less opportunity for growth. Ask the hiring manager how much autonomy and support you will have.
It can feel strange thinking about next steps when you are interviewing, but this is precisely the time you should be thinking of such matters. For example, is your future boss’s job something you would aspire to doing yourself? If not, does the company provide good internal opportunities? Some companies allow analysts and data scientists to move freely between teams as their skill set is highly portable. Others put a greater emphasis on domain knowledge and may not provide this mobility. In any case, think ahead. It is fine to come to the conclusion that after this role, you would like to work somewhere else entirely.
By asking the right questions you should get a sense of whether this opportunity is a fit. Trusting your gut can be valuable here. You should be excited by the scope of the role and also a little bit scared. Being at the edge of your comfort zone means you will be able to learn and grow. If you think "this job is going to be easy," we would advise walking away. Life is too short not to learn something new every day. Pick your role carefully and never confuse confidence with excitement. "I can do this" does not mean "I want to do this."
Choosing your next data science role or analytics role is not easy. This is a fast moving space where roles and their associated expectations are rapidly evolving. That said, we hope that in these four posts we have at least covered the key factors to weigh when considering your next role. You might not get answers to all of them, but with a bit of luck – and perseverance – you should be able to address most of them in a satisfactory manner. What you do with that information is up to you. After all, you are an analyst! Algorithm, visualisation, or spreadsheet, you surely have the tools to make the decision.
If this series does help you make any life changing decisions... do let us know!
– Colm & Ryan