What Data Science Job is for You?

Data science. Sexiest job of the 21st century, right? 

You know it. 

Everywhere you look, companies are hiring for data scientists, the apparently elusive expert that deals in all things data.

But look a little closer at the job descriptions posted. Compare a few, do they look like the same role?

Maybe some do, but in a lot of cases you’ll see some pretty varied ideas of what a data scientist is actually supposed to do, and therefore what experience and background is expected.

This can make it particularly hard when you’re trying to figure out your next career opportunity; where to take your data-led career. You open a link to a data science role, only to find it’s completely different from what you thought it would be.

The generic data science job advert

Sometimes, the people writing the advert don’t actually know what they want. The worst job post is full of generic buzzwords, asks for “experience with R or Python preferred”, and some guff about statistics and presenting to execs. All helpful and very relevant skills in data science, I admit, but does it actually tell you what sort of work you’ll be doing? Hell no.

Everyone wants to enjoy the work they do, and everyone has different interests. Data people like us are exactly the same in that respect.

It’s so important to find a job that fulfils you in the work that you do, or you’ll become bored, stressed, to say the least.

Which role is for me?

But maybe you’re not actually sure what it is that interests you. You know you want to do something with data, but what, exactly?

To try and make some of this clearer, we’re going to give a few descriptions of different subsets of the generic data science discipline. It’s not perfect, but there’s so many definitions of data science kicking about that perhaps giving different focus areas specific names will help you. 

There can always be an overlap of skills, and there should be. In an increasingly data filled world where everything can be tracked, curiosity and adaptability are essential traits, and being a hybrid across multiple technical areas can be a superb asset in your career.

Remember, data science itself barely existed as an accepted term ten years ago, yet a lot of the work we do now is incredibly similar. Makes you appreciate the value of trends and hype… 

Anyway, let’s get down to it. Here are five data science roles that might suit you, and a bullet pointed view of what the roles and requirements might be.

Types of Data Science Roles

BI analysis and dashboarding

  • Tableau is your friend
  • SQL is the main data language you use
  • You’re all about clear visualisations and concise reports of performance
  • Regular reporting is your jam
  • You deal with questions from directors a lot
  • You want everyone to use the dashboard instead of asking you questions

Product analysis

  • Have you A/B tested that?
  • Lots of statistical testing
  • Rigorous
  • You can use R or Python to get data and test data
  • SQL is always a constant
  • You work with product owners to understand hypotheses and draw up assumptions
  • You are more involved in the recommendations and development of product

Exploratory data science

  • Deal with vague questions
  • Some modelling
  • Some clustering
  • Tool agnostic
  • A lot of visualisation
  • Understanding is the aim

Predictive modelling

  • Regression techniques
  • Typically have some solid knowledge of statistics and probability distributions
  • You want to try lots of cool models but people don’t understand them
  • A lot of feature engineering and iterations
  • You know some production

ML production engineering

  • Software engineering meets machine learning models
  • Pipelines
  • Data structures
  • Good knowledge of modelling techniques and packages
  • Makes models scalable and efficient

So, which one is for me?

To be honest, only you can answer that question. We’ve attempted to try and provide some clarity on the fuzzy world that is data science, but there are probably hundreds of job descriptions out there that have an overlap of different skills, or something completely different entirely. What you get out of this article is hopefully a better understanding of where your skills and interests lie, and how you can describe this in an interview, or even just explaining what you do to friends!

I want to learn more!

Found your ideal data science job, and prepping for the application process? Brad has written an article on questions you may encounter in an interview, and how to approach them.

If you’re interested in learning more about the concepts of data science in a broader sense, we have an introduction to Artificial Intelligence course available on Skillshare that covers a broad definition of AI, with some history and applications. It could be a great second step to help you discover the path you want to take in your data science career.

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