2 min read
14 Aug

For years I distanced myself from the “data scientist” label without a good rationale and I’m now starting to think again. While I haven’t really come to any conclusion, I’m more comfortable with that description now. In probably no logical order, here are my thoughts:

There’s an obvious argument that labels don’t really matter if everyone knows what they are/do and how to convey that clearly to someone else. “A rose by any other name…” and all that. I’ve had some odd job titles in my time that I think only made sense to the place I was employed by (if at all.) To a large extent that’s because some sort of vertical function, and horizontal hierarchy, and role specialism need to be wrapped up in a catchy one-liner.

When I converted my employed CV/profile into one suitable for a self-employed service provider, I struggled. Because I am relatively new to freelancing, my employed experience was important to include and will remain so until I’ve been independent for longer. But initially, it contained action terms about leading and managing. Without changes, I would not appear highly enough in searches for hands-on contractors / consultants and I’d rank too highly for managerial interim roles that I don’t want to do. The words describing me are important. Maybe job titles don’t matter so much, but keywords certainly do.

I must also be crystal clear that I’m independent and not employed, so you will see words like ‘engaged’ and ‘contracted’ instead and that’s deliberate. When the responsibility for determining IR35 status switches from service providers to fee-payers in April 2020 (private sector, medium and large enterprises) I expect a similar tightening of language in role specifications too.

DataCamp is unequivocal: “the smartest way to learn data science online”

I’ve done quite a lot of courses, but I haven’t really thought that I was doing data science to be honest. I just saw it as learning to do a lot of the analytical process that I do already in R rather than Excel + commercial software. Whilst many people use the R platform to learn concepts and how to code for them, I have been learning to code concepts that are already familiar. It’s less important to me how they are collectively labelled.

But there’s been a side-effect and I’m not sure where it’ll lead. Some of the courses that I’m now doing, are re-introducing me to long-forgotten types of models that I’ve not touched for years since when I worked in market research. Is this something to brush up on again and broaden my offer? I’m also being tempted by topics that I yet know only a little about, like machine learning. Am I heading towards being a generalist data scientist maybe? I can see both sides of the niche-down / broaden-out schools of thought. As a less-experienced generalist I’d have to do more assignments to generate the same income. But if it safeguards against the potential hazards of working for a relatively small subset of clients in one industry it’s probably worth it.   

I’ve also been experimenting with online marketplaces, platforms where freelancers bid on freelance jobs. I was initially curious as to whether someone with my skill-set would find any work. Yes, there’s work there for sure, especially if I am willing to do something a bit different. In those environments, the ‘top match’ category for me to look in are… you guessed it, data science! But not always. Someone posting a data job will only place it in that category if that’s how they label it themselves. I’ve also found relevant opportunities in business support, consulting, marketing, excel, scripts and utilities, tutoring, programming. I can see already that doing small pieces of work on these platforms may be a useful way for me to gain commercial experience in areas I’m less experienced in.

To sum up - to some algorithm developers and training providers, I am a data scientist. Learning some packages in a data science related tool has made me reevaluate what I do now and want to do in the future. How I choose to describe whatever that skillset ends up being is (theoretically) up to me because I’m not constrained by a corporate job title. But if I want to be found, I need to play the keyword game. My curiosity about all the ways in which freelancers can generate income has led me to another environment. In experimenting with where to find relevant opportunities when real-world task descriptions don’t fit into neat boxes, I’m coming across lots of other ideas for how I may be able to put my skills to good use.          

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