Finding the Perfect Analyst

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Data Analyst

Let me start out by saying that the following is intended to be a helpful guide in respect of finding the right people for your business. There are many circumstances where you may need to deviate from the guide I provide and sometimes, it may be down to the level of maturity of your own business, your business plans, or other circumstances that flex the necessary package of desired skills. Use what I set out here as a starting point to think about what’s needed but remember to personalise it to your own specific need.

Finding the perfect analyst

It might seem like folly to worry about finding the perfect candidate for your analytics role, when such skills are already in high demand. However, the old saying ‘recruit in haste, repent at leisure’ is as true as ever. In fact, given the increasingly crucial role that analytics teams and their leaders are playing in more and more organisations, can you really afford to appoint a ‘problem child’? In this post, we look at the range of factors to consider as a hiring manager, with more than you might imagine.

Based on our current work, patterns of hiring and candidate availability, there remains no real change in the basics for all hiring. It should come as no surprise to you that you have to start with a clear role description, followed with a push through appropriate channels and, where necessary partnering with a recruitment agency that understands both what you need and any technical terms you use within your area of vertical domain expertise. Assuming you have successfully completed those and worked with your agency to get down to a viable short-list, what should you now be looking for when interviewing or testing your candidates?

Technical requirements should not be overlooked. Take time to list the systems and technology your hire will need to use; considering database management systems, query languages, analytics software, presentation software and any other specialist skills required for this role. However, avoid the temptation of reducing technical capability down to a list of packages used or syntax understood. This is not an IT role and in fact it’s often easier for a candidate with the right mindset and attitude to learn these aspects of the job. It’s certainly easier for such a person to learn new software packages than it is for someone with the wrong attitude or mindset to change who they are. So, looking beyond the bonus of selecting a candidate who has previously used your chosen packages, what else is there to consider?

One way to think about this question is to reflect on the different responsibilities that such a role entails day to day. Often this can be quite varied. An analyst may be called upon to draw out what internal customers really need, or communicate a compelling case for action, or simplify some technical detail. A useful way to bring this to life is to think of your analytical candidate as in fact fulfilling a number of roles, or having to ‘wear a number of hats’. This can vary from business-to-business and sector-to-sector but common themes are that your hire will need to be:

  • a data scientist (navigating multiple systems and drilling into merged data to find important patterns)
  • a statistician or economist (creating hypotheses and models from raw analysis)
  • a psychologist (using listening and questioning skills to get to the real need and interpreting human behaviour)
  • an artist (from data visualisation to aesthetically pleasing slides, presentation of information that engages readers)
  • a storyteller (grabbing attention with a compelling summary of findings with demands action)

Quite a tall order, to find all that in one candidate, to be sure. The seniority of the role should influence how mature you expect your analyst to be in each capability, but it can be a useful framework with which to explore a candidate’s wider competencies. You are looking beyond a relevant degree and evidence of past analytical work. How could this work in practice? Well, you might like to consider asking:

  • How do you go about gathering the data you need to answer a business question? What technical skills do you use?
  • Give me an example of a time you generated a hypothesis from some initial analysis and how you tested that hypothesis.
  • When an internal customer comes to you with a problem, wanting your help, how do you find out what they really need?
  • Talk to me about (or better still show me) an example of how you’ve visualised data and why that worked for those who saw it?
  • Tell me a story about the most important piece of analysis you’ve undertaken and the difference that made.

It can be very illuminating to hear and see candidates respond to such wider and deeper questions, rather than a series of (often meaningless) technical checklists or bland generic competency based question sets. I hope it helps you in your quest.

Finally, when you have a clear idea of the requirements for a role, and have assessed potential candidates against those, I would caution you to move quickly to build ‘the offer’. Despite the earlier warning not to recruit in haste, analytics is also a skill that is hugely in demand currently. The best candidates may well stand out to your competitors as well. So, it can be useful to use the checks above, together with a thorough role description to enable you to make a decision promptly after an interview. Putting in the groundwork in considering what you really need should enable you to better pounce when the time is right.

Do you need help finding the perfect analyst? Get in touch with MBN who are one of Europe’s leading Data Science, Big Data, Analytics and Technology recruiters –