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The Dos And Don’ts Of Hiring Data Scientists

The Dos And Don’ts Of Hiring Data Scientists

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Data science has become a buzzword lately and with good reason — companies that are analysing their data are fostering more business opportunities and improving their decision-making. And this has been shaping trends around the world, with organisations heavily investing in effective data science techniques and teams to carry out these tasks.

This has driven a huge demand for data scientists in the market; one that has generated a healthy competition between aspirants. But this pressure is not just felt by candidates — companies and hiring agencies are also weighed down by the need to hire people who are perfectly right for the role.

How should you navigate this complex hiring process? While some non-traditional methods have emerged in recent times, this outline might help you streamline the process:



Dos

Articulate Purpose Of Hiring Data Scientists

The draw of jumping on the data science bandwagon may be too great a temptation, but organisations need to first introspect and understand if their company is at a stage where the requirement for one is real. One way to go about this is by identifying the problems that the business is facing and establishing the corresponding data and the potential return on investment they expect on this.

A good place to start would be to identify the intersection of business problems and the information contained in the data. Additionally, depending on the longevity of the problem and the opportunities that it may present, an organisation can even choose to hire on a contractual basis, or work with an independent consultant.

Even here, be very careful in crafting your team. Data scientists come from diverse backgrounds — while some have deep experience in machine learning models, others may count visualisations and analytics among their strengths. What is more, some may not be specialists in a particular field and may perform well in a wide variety of roles.

Therefore, hire data scientists who would be relevant and a good fit for the particular business problem you are looking to solve.

Be Transparent About Their Job Roles

Given the disparate functions data scientists are expected to perform, it may be challenging to classify their role. This flexible nature of the job should be communicated to candidates in a clear and transparent manner.

Without getting into general platitudes, talk to them about the projects they will be involved in, the kind of problems they will need to work on, and what is expected of them. Data scientists are driven by how their domain knowledge can help solve complex problems and most already understand the ambiguity that comes with the field.

Manage Expectations Of Non-Technical Employees

Although not directly related to the hiring process, if not addressed during this time, you might have to deal with the unrealistic expectations that other members of the team would have with this hire. 

It is your duty to quell the notion that data scientists are geniuses who can solve all their problems with a pinch of software and a handful of data. They might be directing the problem-solving process in a project, but this will require a team effort, and this needs to be effectively communicated.

Your team might not necessarily be experts at data science, but help them understand what data scientists can do and how they can help them.

Foster A Data-Driven Culture

Even as most organisations look to leverage the potential value of being more data-driven, a closer assessment might reveal that these companies might not be a good fit for a data scientist to begin with. Some companies may not have strict data and technical support in place, and still, others may find that most employees lack even a very basic understanding of data science.

Cultivating a data-driven culture that leans on data to enhance efficiency can be achieved by shifting the company mindset, strengthening employees’ skill sets with various programs, and by solidifying the company’s datasets.

Assign A Technical Leader

Data scientists need experienced leaders who can steer, support and lead the company’s data science initiatives. They can also help bring accountability and loop in feedback from the rest of the business as and when required.

They should also be knowledgeable enough to understand when a result is meaningless and be able to deftly perform other tasks including gathering customer insights, develop a good understanding of legal guidelines, and pool resources to enable the timely delivery of projects.

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Hiring or assigning for this position should not be rushed, and due diligence should be given to experience over educational qualifications, specifically for this role.

Don’ts

Unprepared Talent Acquisition Agencies

The importance of training recruiters to hire for data science positions cannot be emphasised enough. This becomes embarrassing for the company, especially when an experienced data scientist is at the receiving end of an inane conversation that highlights the interviewer’s lack of subject knowledge. 

Recruiters, thus, should have enough details about the position and not bound themselves to drop jargons that seem to communicate a lot, but does the opposite. 

Action Without A Plan Or Purpose

Analogous to the first point in the previous section, companies dive into this space without necessarily having an understanding of what it is or how it can improve their business. 

And having a purpose and a vision is only the first step. Companies should draw up a clear, detailed plan that will steer the company forward. 

Looking For Perfection In Data Scientists

Oftentimes, unrealistic expectations can lead to an indefinite wait for a candidate that checks all the boxes in a job posting. Some companies want candidates who are gifted in a wide range of disciplines, should have tons of experience, should be skilled in a variety of toolsets and even throw in a PhD requirement.

In all likelihood, such job postings will remain open for months on end since the problem is not the candidates but the expectations of companies.

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