How To Hire A Good Data Scientist: Five Easy Steps?

June 15, 2019 5 min read By Cogito Tech. 1067 views

Data scientists are becoming the key engineers to utilize big data for AI and machine learning. And hiring a good data scientist is a challenging task especially if you need such highly skilled professionals for developing the advanced technology-backed system that requires deep understandings of such fields and can utilize the resources efficiently.

Companies looking to appoint data scientists need to follow a letter-perfect process to recruit the right data engineers that can fulfill their needs. If you or your company is looking for such candidates and don’t know how to hire data scientist you can find out here the several steps to organize the recruitment and selection procedure to appoint such professionals.

Five Steps How Companies Hire Data Scientists

#1 Get the Applications from Multiple Sources

Hiring such highly skilled professionals is not a single-day job, you need to start the hiring process in advance, or you should have enough time to allow multiple applications to apply for this job from various locations and different fields or companies.

Here your company can encourage the employees to attend industry events, seminars, social events, and business meetings where they can meet and interact with others who are looking to work with your company or seeking a career in such fields.

For the meeting with existing employees and get an idea of the work environment and work-related challenges and other facilities provided by the company. And will also help your company to their expectations and field of interest or specialization they have worked before that you can share with your company HR to contact them if find eligible.

Sending the newsletters is another way to invite the candidates to subscribe so that they can get updates about new openings in your company. Many people visit the company’s websites to find out the openings and if your company does not have such vacancies you need to ensure other ways to interact with such candidates.

#2 Detailed Pre-Screening to Filter Candidates

When you decide to hire someone and know what the exact requirements for the candidate you are post the same as a job description on job listing websites. But still, few people ignore the same or properly do not check the actual requirements and come of the interview.

Hence, there should be a proper questionnaire to know the qualification and motivation of the candidate. As such simple self-assessment test helps to filter the candidates who ignored the actual requirements listed for this profile.

#3 A Well-organized Round of Technical Test

Candidates who successfully cleared the self-assessment and motivation test, are invited to perform the next level of challenge containing the trailer of actual work in machine learning as a service that they are expected to perform during their duty hours if they got appointed. This takes the form of a mini challenge performed by the candidates remotely.

Such challenges are suitable for entry-level data scientist positions and candidates will have to face this challenge and clear the second stage to move to the next stage of the recruitment process. And during the whole process, transparency is important. Candidates who clear to move next stage are also informed how many candidates are still left and how many will get through.

While examining these tests we blindly do the applicant’s identity to avoid any unconscious bias like expecting a candidate to be good if he has done graduation from a well-known reputed university. Hence, it is also necessary to define the grading criteria in advance to forgetting one aspect and over-rating others.

Data scientists should be not only considered with their coding skills but also their organization and data communicational skills. And candidates having strong command in one aspect should be not hired if they are weak in other aspects.

#4 Organize a Face-to-Face Personal Interview

The personal interview process should be organized professionally that should last long up to 2 hours while trying to find out the different skills in the candidates like Knowledge, Reasoning, Passion, and Communication that represent their various aspects.

Knowledge skill is important to know whether candidates know the stuff that is required to perform such tasks. Reasoning is another skill that helps to know a candidate can solve the puzzles he doesn’t know in advance. Communication skill is also important for a candidate to communicate the complex ideas clearly to others and the last one passion is important for such candidates so that they can enjoy while working into the field he is applying for the job.

While covering all the four aspects discussed above, the interview process is composed of a conversation with a sequence of increasingly hard questions. However, if all the questions are not answered but still you will get an idea of the candidate’s knowledge and skills in multiple fields that are also required for job positions.

During the interview avoid asking only questions that a “trained monkey” would be able to answer. Instead asking the candidate to explain an answer in more detail or to go through the process on a whiteboard will help to solve the problems easily. And asking too many questions on a particular topic is not good as it can be an area of weakness.

If a candidate is unable to answer your question, don’t embarrass them instead tell them it’s ok to not know everything and change the topic. And also avoid making the interview process of a candidate going bad as the emotional state may prompt bias while showing their knowledge.

#5 Selection and Appointment of the Candidate

Finally, the last stage comes where you will select and appoint the right data scientist from the few top candidates who have cleared all the test levels. Here, you should have the top three best candidates who are different from all the rest. Go through first with top candidates and make sure they are ready to join if they got selected. You should also assume all the candidates interviewed in various other companies at the same time, instead of believing that you are “picking” a candidate to join which is a kind of two-way process.

Also Read : How to Measure Quality While Training the Machine Learning Models?

Now invite the top three candidates to have a meeting with your team and gather the feedback and if possible, go deeper ask questions like personal ambition, cultural fit, and career paths. And finally, you can scare away the candidate by telling him how hard life is at the beginning, and how their life will be different from other large corporates.

If such candidates manage to survive this stage it would be easier for them to work in such an environment. At this point be confident to decide whom to select and make an offer for joining. Though candidates will not go without your final answer and they will ask you why they were not selected and the honest assessment as to whether it is worth them applying again or something else and if you treat them as easily discardable resources you are not supposed to appoint the top talented candidates for data scientists or hire machine learning engineer.

As we have already discussed above, hiring scientists is not difficult that much and if you treated your candidates the way you would like to be treated you will find the right candidate with the right talent your company is looking for while at the same time being recommended by people you not supported. As applying to a company is a hard and life-changing process and should be considered like leaders making a critical decision in their professional life.

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