Incorrect Job Title
The right job title and knowing what the company wants in hiring is a job half done, but an incorrect one can lead to a talent that doesn't match the requirements. We see how companies often use "Data Scientist" as a title, but they need to differentiate what they are looking for. Does your company need someone to build analytics dashboards and track critical metrics? Or to create prediction algorithms? or develop your data ingestion workow? Also, there is a vast difference between an ML Engineer, a Big Data Developer, a BI Analyst, and so on. It is necessary to realize the requirements and define the right job title, to save time and efforts in finding the right talent.
Not Emphasizing on Interesting Problems
Often, recruiters follow a natural path of telling data scientists about benefits and pay, leave policies, and recruitment processes. But data scientists love problem-solving; they generally move from one company to another as they have a hunger for solving real-world problems and scenarios.
Hence, it is a good idea to speak about business problems in brief. It will also help if recruiters can use industry terminologies and talk about technical skills and toolsets. The job needs to come across as an opportunity to learn and work together on technological advancements.
False expectations of Experience
Data scientist job profiles are just a few years old. When companies or recruiters are looking for a Senior Data Scientist, they often expect the data scientist to have a few years of experience. While it's natural in other industries, it can't be right for data science. Many researchers and analytics or statistics professionals have been doing data science every day without being labeled as one.
Traditionally Sourcing Strategies to Hire
Qualified data scientists are in high demand and short supply. A well-thought-out sourcing strategy that will attract the right talent pool is essential. Companies can't expect the data science professionals to jump the ship only based on the job descriptions or a few calls with the company. It is crucial to create brand awareness about your company in the data-tech community. A niche industry such as this works on the connections, and it makes sense to go beyond the traditional hiring strategies and establish your company as a thought leader in the field.
It can be achieved by speaking at conferences or exhibiting at various conferences or events, or participating in webinars. Initiatives like these attract talent and intrigue them to consider open opportunities by the companies.
Approaching Interviews with Traditional Processes
Like hackathons or coding challenges, companies can make the interview process more relevant to the industry. Many research reports show that interviewing alone is not reliable for validating candidates' skills and often causes both candidates and employers. Instead, it will make sense to create a data science challenge in the form of skills assessment that all applicants need to participate in. The challenge can pose a real business problem that stimulates a-day-in-the-life of the job. The result is a valid comparison across candidates and their skill sets that bring out the best and right fit talent of the lot to work with your company.