Post-2020, recruiting for work in data science will be particularly challenging as the cycle requires a thorough evaluation of technical skills. Since these positions are much in demand, the appropriate candidates get various rewarding offers, which decreases the rate of success in hiring. To avoid this, organizations need to have a clear idea of what the company needs and the desires and qualities of every applicant.
Aside from this, employing data scientists is a testing measure because the job description is often misleading. It is necessary to address what issue you want the employee to solve for your organization. For instance, an applicant can be good at AI and not analyzing data. Regardless of whether the candidates perform well in the assessment, they would not fulfill the job responsibilities. Subsequently, it is fundamental to have a fresh and precise description of the job before you start searching for candidates.
Let us see some of the most common data Science Hiring Challenges faced by the recruiters:
1. Lacks Accuracy in the job description
At the point when recruiters need to recruit data scientists, they commit the absolute first mistake while preparing a JD, to be exact in the "job description." You always need to mention the kind of work you are expecting the candidate to solve.
2. Narrowing Down the Job Title
Right after discovering the term "data scientist" 11-12 years prior and as a lucrative profession, it has just developed from that point forward. It has become a more standard job throughout the long term. Notwithstanding, when recruiters post job role for a data scientist, they focus on somebody whose designation says, data scientist.
3. Evaluating Issues
Merely the interviewing, an applicant can never approve his/her abilities referenced in the resume. The ideal approach to pass judgment on the candidate’s capabilities is by taking a data science test.
Job roles that require data science skills:
Data Analyst: a data analyst centers around examining and taking care of the issues identified with data, types of data, and the connection between various data elements inside a business or IT framework.
Data Engineers: Their primary job is to assemble data pipelines to arrange data from multiple source frameworks, incorporating, combining, and purging information; and organizing it for use in individual analytics applications.
AI Engineer: AI engineers are developers that do projects to empower machines and frameworks to learn and apply information in no particular direction.
Data Scientist: A data scientist dissects and deciphers complex digital information, similar to the use of insights of a site, and helps a business make an informed decision.
Information Architect: A data architect is an expert in computer system administrators who keep up a thriving database environment by directing all the related activities to keep the data source.
Business Analyst: A business analyst (BA) breaks down an organization's business documents and domain, its business or cycles or frameworks, and evaluates the plan of action or its incorporation with the latest technology.
Database Administrator: A database administrator specializes in computer system administration who keeps up a productive database environment by guiding or performing completely related activities to keep the information secure.
Information and Analytics Manager: They give guidance to the team of data analysts. They likewise choose according to their experience where every expert's skills will help improve its profitability. They also direct the analytics division, ensuring the reports produced are exact.
These are the job role that demands data science skills. Now the next step is to evaluate these skills in candidates. The best way to assess technical skills is to conduct a skill assessment test. However, to achieve a skill assessment, we need to understand the core skills that need to be assessed in a candidate for a data science role.
Here are the primary skillsets that you need to assess whenever a candidate apply for a data science role:
1. Technical skills: These are the most critical skills a candidate must have. Technical skills like SQL, Python, R program and Apache flash are considered significant technical abilities required to get a data science job.
2. Domain expertise: Another prerequisite of a job in data science is that an applicant must have sound knowledge of AI calculations, data extraction and mining calculations, data wrangling, deep learning, etc.
3. Arithmetic and stats: These are important for each activity in data science. An applicant must have excellent knowledge of stats and mathematics.
You have to accept that majority of the candidates will not be an expert in all three areas. Hence, it is upon you to prioritize the skills and hire them based on the job you want them to do.