Startups and for that matter, even bigger organisations are always on the lookout for good data scientists for their growing team. While it is often a confusing question on whether one should start a data science career with large firms or startups, which we dealt in our earlier article, it cannot be denied that candidates usually like to join big brand names to start their professional journey. There are many factors that come into play such as exposure to data, an opportunity to develop new skills, learning opportunity and more. While the weights are higher on the corporate side of it, startups can also go about appealing the data science community to get the best of talent.
Set Out Clearly Defined Roles: It is often debated whether startups actually do need data scientists as much as larger organisations do. That is because their roles are often scattered and poorly defined. While there is no doubt that startups will get them exposed to a lot of newer avenues, it is always better to have a job role defined for the potential candidates to know that your startup is ready to go for data science capabilities in the organisation.
Providing Data Scientists With Challenging Roles: Most data scientists are often in a lookout for interesting and challenging roles to take on. Problem-solving is one of the key skills that data scientists have and knowing that your startup will provide an opportunity for them to splurge on challenging problems will have them set out to take the role.
Paying Data Scientists Adequately: There is no denying that paycheck is one of the most appealing factors for any job role. Especially for data science job roles, the expectations are slightly high given the high demand it has. Also, the pay should match up the standards of larger organisations, if not more. Startups with high paying data science job roles may be one of the most appealing factors for the candidate.
Designing The Interview Process In A Way To Sell The Job: More often than not, good candidature is lost because the interview process is not designed to attract the best of talent. It fails to sell the highest-quality candidates on the role. To get the best of data science talent, it is important for the startups to not make it look mundane and tedious. It should be designed in a way that gets their instincts set of the job role on the go. Also, the results should be declared faster for the fear of them not losing interest in the job and finding other options.
Involve Data Scientists In Decision Making: Startups have a lot of crucial decisions to make in their initial days right from hiring to reaching out to venture capitalists, among others. An assurance of involving the data science candidate on important decision-making process in a startup will give them a major boost to take up the role. It gives a sense of involvement which might act as a major deciding factor.
Providing Data Scientists With The Right Team And Infrastructure: Access to the right quality of data and suitable infrastructure to work with such data is one of the major factors for data science to opt for a job. The infrastructure also needs a level of agility. Most data science projects are short-lived, and to maintain productivity, the infrastructure needs agility to support short-lived projects with high failure rates. This means that data infrastructure, data science platforming, automation and DataOps are crucial problems not just for the delivery of business outcome but also for the retention of the team long term.
Focus On Data Strategy: Data science is part of the ongoing data transformation of businesses to become data-driven. This incurs a lot of change which requires strategic alignment and buy-in from the most senior levels. Data science requires a business motivated by a long term vision rather than short term goals. If the candidate has a long-term goal defined, it can help them pick the job role at your startup.
Carry Out Data Science Training At Regular Interval: It will be a major boost for the data science employees at the company as they might foresee it as a chance to re-skill. It comes as an appealing point, for the data scientists to keep growing and exposing them to newer skills in the field. Many larger companies support re-skilling as one of their main advantages and if startups start providing the same kind of exposure, there might be no looking back for them to attract great data scientists.