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Getting into a Data Science career


Katie Russell

Katie Russell

Data Science, BI and Data Availability @ OVO Energy

analytics data science

Getting into a Data Science career

Posted by Katie Russell on .

analytics data science

Getting into a Data Science career

Posted by Katie Russell on .

A brief departure, perhaps more of a tangent, from the blog series on Analytics at OVO, to share some insights from a careers panel I was at last week. This panel was part of an Operational Research Society event for mathematicians, analysts and data scientists at early stages in their career. (Full disclosure, I wasn't part of the panel - I was there to talk on building machine learning products with energy disaggregation. But the careers panel on the same day had some really excellent advice.) So, if you're new in your analytics career, as were the attendees at the OR event, this post may be of interest.

And if you haven't come across it, Operational Research (OR) is a field of applied mathematics known as ‘the science of better decision making’. So, a very relevant field to anyone applying analytics and data science to a business context.

The panel question that struck me was:

How can a recent maths graduate apply for an Operational Research Analyst job when mostly Data Scientist roles are being advertised?

The question author then went on to clarify that as a graduate from an OR course, they could only find Data Science jobs advertised, but felt that although they were not an expert in Machine Learning, they would have a lot to offer a business wanting to use data to make better decisions.

The panel, which included these plenary speakers, gave excellent advice. It really struck a chord with me, especially in the context of my recent blog post - What's the difference between a data scientist and an analyst?.

Their advice was:

1. See beyond job titles

There was broad agreement that terms like 'Data Science' and 'AI' are a bit of a current trend. Not a fad per se, just the current most popular terminology applied to roles in data and analytics, without necessarily having much consistency across the industry yet. The panellists agreed that some companies looking for 'data scientists' have problems that don't necessarily need a machine learning algorithm to solve. Most of all, the message was 'don't be put off'.

2. Showcase your innate skills

The panel agreed that the best companies are looking for candidates with:

  • Potential to be versatile
  • Innate curiosity in data and problems
  • Willingless to learn

I would add that the latter skill should be paired with:

  • Patience to practise

And at all times a data professional needs:

  • Tenacity in finding a solution

3. (Have a plan to) have the relevant technical skills

I admit, the panel didn't cover this explicitly, but I personally think it's worth highlighting the importance of having proficiency in the technical (computing) skills relevant for the business you're applying to, at least if the specification demands it.

Some people can profess to be 'language agnostic' - i.e. learn any programming language. If you're one of those, that's absolutely great, but I'd still encourage you to have a plan for how to get up to speed with the tech stack of choice for the team you're applying to.

You want to give yourself the best possible chance, and therefore as few concerns as possible.

4. Work out what your future employer needs

Finally, an absolutely cracking piece of advice. Read the job specification. Learn about the company and the problems it is trying to solve. So that you can ultimately think about what companies might need from you rather than just what you know.

They suggested, you can call yourself what you like, but you should think like a consultant. Then you can use your additional expertise (say, background in queueing theory, or experience fine tuning numerical algorithms which solve initial value problems) as a differentiator. Showing how you'd translate your knowledge into a business application, is a hugely valuable thing.

Upcoming Analytics posts

Thanks for reading! And if you enjoyed this post, do go ahead and read my introductory post on Analytics at OVO, which will link to the rest of the series.

Coming soon, we'll share our approach to creating a best practise guide within the business, a consolidated skills matrix for analyst and data scientist roles, and by request we'll also dive a little into the team set up at OVO and how we share analytics learning.

For now I'll leave you with a question: What's the best piece of career advice you've been given?

Photo by M. B. M. on Unsplash

Katie Russell

Katie Russell

Data Science, BI and Data Availability @ OVO Energy

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