Five Common Mistakes in Hiring Data Scientists

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Data scientist is one the hottest job in the tech town, making profile important for data companies or fellowship working on AI or machine learning based technologies. And in coming years the demand for such professionals is going to rise into many folds.

Actually, the job profile of a data scientists is quite sophisticated, as it becomes hard to define company’s why need data scientists or what exactly they are going to work with them. Hence, hiring data scientists becomes a challenging task and few companies do mistakes while recruiting such professionals. Hence, we have highlighted here what are the major mistakes to be avoided while hiring a good data scientist.

#1 Inexplicit Title of the Job/Profile

An imprecise job title creates confusion among the candidates who are searching the job in this profile. Companies basically use “Data Scientist” in the title to make the eye-catching for job seekers. Actually, it is much more that merely dealing with data, he can build analytics dashboard, track metrics and create a data sheet for further use.

In fact, there is a huge difference between data scientist, machine learning engineer, big data developer, business intelligence analyst, data engineer, data analyst, data operator and data quality analyst. Though, all deal with certain types of data but having different jobs depending on the needs. So, you need to concentrate on finding the most suitable title for job profile.

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#2 Failing to Highlight the Crucial Problems

The experienced data scientists are attracted to more challenging problems with latest technology and algorithms tools. From natural language processing to computer vision and fraud detection or deep learning all such technical grounds are deal by them.
Emphasizing on the interesting problems is essential part of hiring the data scientist that allowing the companies deal with multiple level of problems comes while working on various crucial projects associated with data science. It also let the data scientist work with the opportunity to learn from others having expertize in complementary fields.

#3 Defining The Experience Too Broadly

The data scientist profile came into limelight few years ago, and in the past few years it became into the mainstream job. And if you are looking for experienced data scientists or detailing the job requirements with experienced data scientist, which a major mistake.

As, many other professionals working biostatistics, quantitative finance, high energy physics etc. are also doing the similar work but do carry the data scientists title. You can find immense underutilized supply of employees seeking experienced data scientist. Hence, before you define the experience too narrowly, take your time and go through in-depth conversation with potential candidates who can go beyond discovering about specific needs for the position.

#4 Incompetent Skill Validation Process

Interviewing the candidates alone is not a reliable to validate the skills of candidates. Instead, while hiring such candidates, employers should develop a data science questioning that applicants need to complete successfully.

And such questioning must contain the real business problems that stimulate a-day-in-the-life of the job as much a possible. Such challenge also exhibit your company culture and how exciting working with your company if got selected for such profile.

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#5 Undistinguished Sourcing Strategy

Qualified and experienced data scientists are in demand but their availability is limited. But if you organize well-defined outsourcing strategy, will attract the talented candidates. It also very important to build brand awareness in the data-tech community.

A good sourcing strategy can take time but investing in such plan of action can meet your hiring needs. And you will not only manage to recruit the right data scientist but can help your company as an exponent for the data scientist industry which also helps candidates while choosing between your company offer or from other nearest competitors.

Cogito is one the companies can help you in hiring the right candidate for data scientist profile as per your requirement. It is involved in hiring machine learning engineer and providing the one-stop solution for training data requirements for AI-driven services. It is expert in supplying the high-quality training data sets for different industries at affordable pricing.

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