Companies must use data to run and grow their day-to-day operations. The core purpose of data science is to assist businesses in making faster and better decisions that can propel them to the top of their industry, or at the very least, be a matter of long-term survival – especially in the harshest red oceans.
But to hire data scientist and building a robust data and analytics team can be a daunting process, thanks to the demand. But with an understanding of the types of data scientists, hiring recruiters can better evaluate candidates on skills needed to fill the role.
Six Essential Things to Look For Before you Hire a Data Scientist
These tips will help you construct a more creative hiring process — by identifying great candidates and reducing the risk of losing them.
1. Know Who they Are
Data scientists commonly have credentials in three key areas: mathematics/statistics/machine learning, coding/software engineering, and expertise within the industry they seek employment in.
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Apart from these, a dependable data scientist will be proficient in scientific research processes and possess impeccable communication skills to give results and provide accurate business solutions.
2. Know Why you Wish to Hire One
There are several ways to use data for any business, but the goal is to identify the core business problem with data science.
For example, for a budding e-commerce company, recommendation engines and knowing how to point consumers to the right products is the core data science concern.
On the other hand, a manufacturing or logistics company can hire data scientists to optimise supply chains. Again, knowing the core issue will allow you to define the parameters of the role you’re hiring for.
Some organisations also look at combining various aspects to derive meaningful insights. But again, the idea is to identify opportunities you would like to see a data scientist handle.
3. Skills to Look Out for
Evaluating candidates’ skills will help you shortlist candidates to work on more in-depth projects or hire data scientists for entry-level positions.
When you hire a data scientist, they require being familiar with the entire process, from the ideation of the problem to finalising and completing it, including the iteration of the techniques. So you want someone very good at defining the problem, figuring out what they want to solve, and how they’re going to measure the outcome.
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Your ideal candidate is someone with a very high degree of scepticism. For example, an expert data scientist doesn’t believe in taking answers at face value and will reflect and question repeatedly.
4. Emphasize A Data-Driven Culture
Know the answers to how your data science team performs and what challenges would you want your candidates to handle. Then accordingly, lay out a hiring process that puts your candidates into an environment that resembles what their day-to-day’ would be.
The goal to hire a data scientist is to create a process wherein candidates are given problems that reflect the real challenges.
Ensure that the hiring process aligns with the team’s culture so that each candidate gets a real taste of what it would be like to work in your organisation.
5. Determine their Fit
Once you bring a person in for an interview, ask them questions more along the lines of how they would do something rather than the definition of a particular concept.
You want to set them to describe the most complex problem they’ve solved. You’ll eventually find out if they’ve solved a problem that didn’t have a well-defined answer. This is significant if someone doesn’t have a lot of visual experience on their resume.
If they discuss concerns where there wasn’t a well-defined outcome or came up with a project themselves, that tells you that they’re able to think through the entire process.
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During the interview, look for ingenuity and the ability to develop creative answers and decompose a problem. In addition, it is essential to hire a data scientist that knows how to communicate with different parts of the organisation, from engineering to business leaders and analysts across the organisation.
6. Look Beyond the Data
Make sure not to hire data scientists with only hard technical skills. The future employee would eventually need to work as part of your team and supply the skills your team is currently missing.
An emphasis on detail orientation is going to go a long way. For example, an essential way of assessment could be explaining two to three statistical concepts to a non-technical audience.
The value of this is twofold; it assesses their communication skills and their critical thinking. A good data scientist should take a typical idea and explain it in layman’s terms.
Different factors guide every candidate, so you must listen carefully and direct the conversation to their areas of concern or interest.
The Takeaway
The field of data science has its unique complications, such as working with messy data and using technical skills to create practical insights.
The ultimate goal is to develop solutions that someone who doesn’t know anything about data science can use.
Being a sought-after job, strong candidates often receive an average of 4 offers, which pulls down the hiring success rate. So make sure to have prospective candidates go through the process quickly and close the position faster.
CodeQuotient works with companies seeking to hire the most eligible tech professionals. We train talented coders into job-ready professionals via our SuperCoders programme who’re ready to take on any corporate challenges.
Contact us today at info@codequotient.com and see how your company can benefit from our recruitment services.
