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Hiring Data Scientists: What and Where to Look For and How to Assess

Data Analytics Researcher, ScienceSoft

3 min read

Optimized supply chains, improved production efficiency, personalized customer experience, and boosted sales effectiveness are just some of the gains that our customers pursue when they turn to data science consulting services. Facing the growing demand for data science talent, we, at ScienceSoft, decided to cover a burning topic of how to hire data scientists. Here, we answer 3 main questions: what skills a data scientist should possess, how to assess those skills, and where to search for the right person.

how to hire a data scientist

What data scientist do you need?

To narrow the initial list of candidates down and make the shortlisting pipeline more efficient, we recommend that you clearly define a needed data scientist’s profile. With a vast variety of skills that a data scientist is expected to possess (including the endless list of big data technologies and machine learning algorithms), you can never find a data science unicorn who handles all these things with the same mastery.

So, you can devise an ideal data scientist’s profile on your own or find the appropriate option among the existing classifications. For example, ScienceSoft adheres to a classification that recognizes 2 data scientists types: analysts and technicians.

How to assess data scientists’ skills?

The approach to skills assessment depends on which of the 3 scenarios listed below your company favors:

  1. Growing in-house data science capabilities (this scenario also covers team augmentation).
  2. Resorting to data science consulting services (when you hire an external consultant for knowledge transfer to boost the development of your internal data science capabilities).
  3. Outsourcing data science (when you don’t plan to develop in-house data science capabilities).

Approach 1. When you search to grow in-house data science capabilities.

  • Check the candidates’ CVs.
  • Challenge a candidate with a test to validate their skills.
  • (Optional) Organize an in-house data challenge (for example, the way Airbnb does).

Approach 2. When you search for a consulting/outsourcing partner.

  • Check a candidate company’s competence and experience: study their portfolio of implemented projects, check the attained partnerships and certificates.
  • Ask to deliver a proof of concept (for complex projects).

Where to find a data scientist?

Now, when you know whom to chase, let’s discuss where to search for. Job sites, recruitment agencies, and professional networks like LinkedIn is the triad that easily comes to mind. However, considering the shortage of data scientists, these traditional resources may turn out to be insufficient. In addition, these channels are mainly tuned to hiring data scientists for growing in-house teams. If you consider data science consulting or outsourcing rather than team augmentation, ScienceSoft recommends turning your attention to three extra sources:

  • Tech communities like GitHub and Stack Overflow – you’ll find the profiles of data scientists there.
  • Listings, like this one featuring the best data science consultancies.
  • Homepages of data science consulting and outsourcing companies where you can check the service and project portfolio of a certain vendor.

Let the effective search for data scientists begin!

Now, you know what these fantastic data scientists are and where to find them. We hope that our tips will help you make your hunt for data scientists efficient and fast, and your data science-powered projects a true success.

Bringing data science on board is promising, yet difficult. We’ll solve all the challenges and let you enjoy the advantages that data science offers.