Some tips of finding and hiring your first data analyst.
How to Find and Recruit a Data Analyst
Estimated reading time: 5 minutes.

With an ever increasing amount of data at your organization, you’ve found the need for the skills of a data analyst to make sense of it. If you’ve never hired an analyst before, hiring your first one can be challenging. A data analyst position is one of the hardest roles to fill. Not only is there a general lack of talent, but also assessing a candidate can be difficult. A one-size-fits-all approach does not work because the requirements are very specific to your business needs. In this post we are going to give you some tips on how to narrow your search.

1. Live Where the Data Community Lives

The best data analysts are constantly networking and building their brands online. They are curating, sharing and contributing content relevant to the data community.

One of the richest communities out there is LinkedIn, especially LinkedIn groups. While engagement on LinkedIn groups can be low, you can still browse through profiles of active group members and contributors. Reaching out to people in these groups can turn your cold outreach into a warm outreach by referencing a conversation or content that person has shared.

Another avenue where curious and engaged analysts hangout is on sites like Quora or Cross Validated (Stackoverflow). These sites give analysts a platform to be seen as thought leaders by giving back to the community of aspiring analysts or people looking to get into a data role.

Offline, these data analysts are attending and presenting at data science meetups. These meetups are great opportunities to prospect and find analysts – both junior as well as established analysts. The conversations at these meetups really help you get a sense of not just the candidate, but also the relevant skills and tools that are hot. You can use these conversations to identify trends that can help further narrow your search of the ideal data analyst for your organization.

Whether it’s online or offline, hanging out where data analysts meet is essential to have your pick of the best candidates out there.

2. Evaluate Projects, Not Resumes

If a candidate writes “descriptive analytics” or “pivot tables” on their resume, how does one get context as to how they can put these into practice?

The answer is Projects.

Projects put skills into practice and communicate a data analyst’s interests.

There are many public data sets that data analysts use to build their skills and portfolio. From the more rigorous Kaggle data science competitions, where companies will pay you to solve their data science problems to personal analysis of Foursquare/Fitbit data – projects help you differentiate candidates.

If you have a large enough data set you can make public, hosting a Kaggle competition can not only help your business but can also be a great source of highly qualified inbound candidates.

Projects help identify talent with intellectual curiosity in different subject areas. You can find projects that align with your product or data stack to seek candidates that have similar interests that they could potentially work on in your company.

If you have a more junior candidate that may not have a lot of published work online, you can have a discussion about an analysis you are working on and what model you are building to test their understanding. You’re looking for answers that showcase their understanding on different data types, analysis methods, statistics and basic programming to solve data problems. These candidates will need more ramp up time than others – but if their answer is what you’re looking for then you can hire them knowing where to invest in their growth for the next few years.

3. The Best Data Analysts are Storytellers

Data analysts who can communicate data through a story win over stakeholders. Simply talking about disparate data points will not take you very far. Data has a story, and telling that story is a necessary skill. It requires understanding your business and market realities.

A change in data should convey some kind of emotion. A big part of decision making is convincing others to adopt your point of view. Sometimes hard numbers are enough. More often you need something that connects the audience to the data on an emotional plane in order to gain buy-in.

Stories add this emotion to data. Storytelling lets you talk about how the data relates to people and scenarios. You can inspire new ideas. You can galvanize supporters for a cause.

Ask candidates to tell you a story using a graph or chart. Focus on how much they focus on the quantitive numbers and how much they give context to help understand the bigger picture.

Data gives you insight, but the story behind it gives you context and the emotion to take action. It’s a skill that every data analyst must have.

Hiring Take Aways

When seeking top data analyst talent, there are many variables you need to consider. The tips we’ve shared should help you prequalify candidates, before moving on to assess their technical aptitude. If you think there’s another tip you want to share, please leave a comment below.

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