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Hiring the right data scientist for the organisation

Any organisation needs talented, hardworking and skilled employees irrespective of department, business unit or a team. But finding and nurturing such talent can be challenging sometimes. When it comes to data science field, with rapid change and demand in the technology, many organisations have set up the data science teams. A successful data science team has 3 major strengths, A-availability of data, B- infrastructure and most importantly C - the “right” data scientists. 

The biggest risk for a data science group is hiring wrong data scientist. The wrong hire will lead to failure of the projects and will also harm reputation of the team within organisation. Hiring wrong talent is not only ruinous in terms of costs but is also damaging for new data science initiatives of the organisation. In this article, I suggest 3 tips to circumvent this situation.

  • Right Selection Process
  • Knowledge and Passion over education
  • Scientist attitude with business acumen

1-   Do I have right selection process? 

The data scientist should go through multiple rounds of interview before final selection. The first round should involve solving a business problem with sample or masked data. This has to be an assignment and a solution can be submitted in ~1 to 2 weeks. Hiring manager may let candidate chose his own preferred coding language and reporting tools. In case of lateral hiring, the candidate may need to work on a problem after office hours. A week or two time would ensure that they get enough time to think about innovative solutions and coding. Even if the candidate is not selected in later stages, organisation gets the benefit of listening to new ideas to solve a business problem.

Second round will be explaining the solution to the interview panel. This will make sure that the candidate is also able to explain the solution to the business stakeholders in future. The same interview can be extended further with a regular technical round.

Data science project is a team work where we need to work with data engineers, domain experts, business leaders and your own data science team members. The final round would be HR round interview where panel can check the person organisation fit and right attitude. This interview can be conducted by experts in this field.

2-   Knowledge and Passion over educational qualification

When it comes to hiring good candidates in technical field, the first choice of many organisations to look for candidates from premier institutes. But this should not be the ‘only’ criteria while shortlisting. Data science is a new, emerging and evolving field. Most of the development in this field is driven by people who have passion of data science. These people have gathered knowledge in ways which are unconventional.

In case of college campus recruitment, passion and knowledge can be assessed from participation in competitions / hackathons, github profile, research papers etc. This is also true for the experienced professionals whose expertise lies in some other field but now they want to enter in to data science. If the candidate has previous work experience in data science then evaluation could be combination of past projects and the self-learning. The goal here is to get data scientist who are self-motivated and eager to learn new technologies and concepts. I believe that combination of passion, knowledge seeking and hard work outshine just an educational qualification.

3-   "Scientist" attitude with business acumen

In the end what we are looking in to candidate is traits of a good scientist such as curiosity, clarity, creativity, etc. But for a data scientist who will be working in a business setting should also have a business acumen. Data scientist should be able to translate the model output in to business benefits, let’s say either cost savings, revenue generations or work efficiency. This might not directly apply entry level data scientist but if we are hiring a senior resource or leaders, the person has to interact with various stakeholders and explain the model in the simplest but business language.

As a closing remark, I understand that finding ‘jack of all trades’ is like finding needle in haystack. Some of the points or skills requirement can be relaxed as needed. A key takeaway is, risk of hiring a wrong data scientist can be mitigated if we have right selection process ensuring candidate has right attitude and skills to help organisation succeed and prosper.

Please let me know your thoughts and suggestions in the comments below.

Disclaimer: The views and opinions expressed in this article are those of the author's and do not necessarily reflect the official policy or position of current or previous employer, organization, committee, other group or individual. Analysis performed within this article is based on limited dated open source information. Assumptions made within the analysis are not reflective of the position of any previous or current employer.

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Tags: #datascience, #datascientist

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