Data science skills are considered to be most sought after in data-driven organisations. While the right data scientist can assist the company in achieving its business objective, a misfit professional can have a negative impact on the firm. However, unlike software developers, hiring data scientists can be strenuous and stressful for startups. Consequently, startups should be cautious while hiring developers who will be an integral part of the business.
Today, hiring a proficient data scientist in startups is increasingly tricky as developers in the industry also have similar skills. To make the matter worse, often they even have similar projects too. Therefore, determining the right data scientist is crucial for startups to avoid any deterrence in their business operations.
Here are a few best practices for evaluating and hiring the perfect data scientists for your startups.
Focus On Data Science Skills You Need The Most
Writing an appropriate job description is vital to get the desired application. Often, the job posting scares data scientists as it includes almost all the skill present in that domain. This decreases the chances of getting candidates with desired knowledge. Although irrespective of the job description, most of the aspirants apply for the role without analysing the requirements. But the idea behind a concise job description is to get the appropriate application of aspirants relevant for a specific role within the landscape. Undoubtedly, figuring out candidates who applied solely for a position mentioned in the job description is still challenging. Still, such practices will increase the chance of hiring the candidate you want.
For one, if your business operations mostly engage with video files, then mentioning computer vision and image processing instead of including other similar skills like text analysis is what you should cling on to. Finding a ‘jack of all trades and a master of none’ can afflict the business and may also lead to financial and customer loss. And therefore, startups must seek relevant skills.
Ability To Convert Business Problem Into Data Science Problem
“Converting business challenges into programming problems is an essential skill that is needed in startups,” said Santosh Rai, AI architect and a head data scientist at ProVise Consulting. And not just startups, this ability is required in any company as data scientists help in assisting decision-makers in making business decisions. Consequently, along with data intuition skills, having someone who can make business problems straightforward and then solve it through data science skills is crucial.
Look For Someone Who Can Build Data Pipeline
As productivity is essential for startups, it would be ideal for you to get a developer who can devise data pipelines. Although data pipelines are considered to be the job of data engineers, getting hold of a developer who can streamline data science processes will be hugely beneficial for startups. A robust data pipeline is vital to gain operational resilience within data-driven organisations. Therefore, to carry out the development process effortlessly, hiring developers who can assist in creating data pipelines will be an added advantage.
Projects Not Certificates
To understand the expertise of developers in data science, one should evaluate diverse projects in an applicant’s portfolio. Certifications cannot demonstrate the skills they have obtained; putting the knowledge into use is what makes the difference. Therefore, having a candidate who has hands-on varied projects will be ideal for startups.
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“With changing times, the industry requirements for data scientists have evolved too. Previously organisations were looking for candidates with specific certifications that were required for working on vendor-specific platforms. Today, the industry is looking for data science stack skills in developing programs that require augmented certification along with hands-on experience on projects,” said Vishal Chahal, Asia pacific leader of the technical elite team for data warehouse & AI at IBM.
Apart from the practices mentioned above, it would be best if you looked for developers’ achievements in the domain. Startups can also analyse the expertise by looking at their contribution to Kaggle, GitHub, StackOverflow, and LinkedIn to determine both technical and soft skills of the developers. “Today soft skill has become the differentiating factor as often data scientists have to explain factors behind their outcomes to stakeholders,” said Sharath Kumar, a data scientist at IBM India Software Labs.