A guide to choosing the right data science position

A guide to choosing the right data science position

With examples from real-life data scientists from all over the. Here is a list of common types of positions for data scientists. You can find the link to the episode where I interview the guest with that position below the titles.

With examples from real-life data scientists from all over the world

I have been interviewing people from all around the world on my podcast So you want to be a data scientist?. My guests have been from very different positions who all have either the title data scientist or similar. Inspired by everything I’ve heard from my guests on the podcasts, I prepared a list of possible positions you can consider when planning your future.

It’s important to know which one you want to work at, in order to get the most satisfaction out of your job. I understand this is not very easy to do when you have little to no experience in a field. In data science, you can end up in many different types of positions. They all have varying responsibilities, different careers and busyness. But if you are not in the type of position you enjoy, you might end up unhappy.

I didn’t think about this much when I first started. For me, it was a trial and error approach. I have started in a big company as a consultant, only to realise that wasn’t for me. In time I realised an in-house position would suit me better. That’s why I went for a change and I will be starting in my new position next week. I’m happy and excited to experience how being an in-house data scientist will fit me.

If you don’t want to spend a couple of years of your life trying and erring as I did, here is a list of common types of positions for data scientists. You can find the link to the episode where I interview the guest with that position below the titles.

Consultant data scientist

Talking about data science in consulting with Madli Kivisik ‍

Typical responsibilities: Being a consultant means that you will be working with clients of your employer. Your assignments are projects on client companies. You will be doing data science either in a team or alone in a different location/team than yours.

There are typical responsibilities of a consultant other than the technical data science tasks. These are mostly, helping with coming up with a business scope for the project, communicating with the client about what you’re doing and why it’s important, talking to business-people periodically to update them. It is a very “soft-skills” heavy position.

Projects: Projects are begun by salespeople of the consulting company going around pitching clients. You can get any sorts of projects. By this I mean, they can be in any industry. Think of the energy industry, hospitality, banking, finance, travel. Of course, the industry will depend on which companies your main employer is serving. It sounds interesting to have all the options but it is also likely that for every project you enjoy working on you might get assigned to a couple of projects that you won’t.

The projects might also be at any level. Even if you want to work on NLP, you might be stuck working on making dashboards months at a time. So make sure you understand what type of projects are done in a company before you start working there if you don’t want to be disappointed.

Career advancement: A consultant has both business and technical skills. So you have the option to grow into a managerial role or a more technical role.

Working hours and stress: Working hours can get challenging in consulting. You are not working directly for your employer but a client and consulting companies want to look good to their clients. This means that you might get extra pressure from your employer when it comes to deadlines.

What might have been a soft deadline in an in-house position, might become a hard deadline in a consulting environment just because your company promised the client to deliver at a certain time. This might cause a high-stress environment. But still, some companies do a good job managing this stress. Though I get the impression that working long hours and being fine with stress is “expected” of consultants.

Pros — projects in a variety of industries, mostly good pay, you get to learn the business side of things, actively using your soft-skills

Cons — you might get stuck with the type of projects you don’t like, working hours might be longer than average, the stress of working with a client

careers data-science data analytic

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