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Advice for Analytics Recruiters and Recruitees

jigsawThis article is the part of Analytics Placement & Salary Report 2013 by Jigsaw Academy and Analytics India Magazine

Data scientists require a strange mix of skills – ranging from logical think to programming expertise to statistical knowledge. Finding someone with the right combination of these skills is a big challenge. We spoke to some business leaders who have hired large numbers in analytics. Here is some recruitment advice from these experts.


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When recruiting a data scientist, begin by ascertaining where you are in your evolution of having integrated processes/systems/data feeds. Next determine what you’re trying to accomplish. Candidates could have different strengths, so select those that can optimize your needs and objectives, based on where you are and where you want to be.

As an analytics recruiter you already know two things:

1. Hiring data scientists is hard, mostly because there are just not enough of them out there. The field is relatively new and there is no one test they can take that will definitively say if they will make a good data scientist or not.

2. It’s going to cost you- Data scientists command big salaries but the value they add to your business will justify their existence.

[quote style=”1″]Successful analytics and big data initiatives are largely dependent on finding the right talent.[/quote]

Well nevertheless, as an analytics recruiter you need to hire exceptional, effective analytics talent. And to do that you need to remember that:

  • Creativity is what truly makes a great data Scientist. Yes they need to be knowledgeable about machine learning and statistics, but look beyond analytical talent and computational ability.
  • You need to pay them market standard salaries. If you don’t they will leave for you for a better pay the first chance they get.[quote style=”1″]If your candidate is creative and observant, has data visualization skills and can collaborate and persuade effectively – you should definitely consider her.[/quote]
  • Your recruit needs to be accepted as a peer-level stakeholder who will have an impact on every other division in the organisation, the leadership and the culture of the business. Ensure that the rest of your workforce has the cultural ability to absorb and accept this strategic change.
  • You need to find ways to measure the ROI of your Data science team.


Recruitees- You Are More Likely To Get Hired If:

  • You have an advanced degree in a quantitative field; hands-on experience hacking data (ideally using Hive, Pig, SQL or Python); good exploratory analysis skills; the ability to work with engineering teams; and the ability to generate and create algorithms and models rather than relying on out-of-the-box ones.
  • You can showcase your analytical and creative skills in the interview itself.
  • You know R.
  • You invest in additional skills that set your apart from the crowd such as text analytics or HR analytics.
  • You stay up-to-date with current research trends and innovations, not just in analytics but within the general corporate world. Subscribe to academic journals, business magazines and online forums. Read and comment on blogs by thought mentors in the industry. Start a blog of your own. [quote style=”1″]Showcase your data analytical and creative skills in the interview itself[/quote]
  • You understand that data analysis positions come in many different levels of scope, skill and responsibility. Analytics jobs could be posted under business intelligence, market research and technology analysts, so it’s critical to not discount them. Target jobs that will match your skills and remember that while each job is different, there are certain skills that they all share.

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Gaurav is an MBA from IIM Bangalore with over a decade of experience in the field of analytics. He has worked across multiple verticals including financial services, retail, FMCG, telecom, pharmaceuticals and leisure industries. Currently, Gaurav runs Jigsaw Academy, an analytics training company. You can also visit his blog –

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