COVID-19 is a tormenting time for businesses and their financial stability, where companies are looking not to make any wrong investment to keep up their sustainability. And at this time, if any organisation is taking a plunge into data and planning to transform its business strategies with data science, it is critical to keep a few things in mind.
Data scientists are indeed in much demand and hard to find amid the crowd; thus, companies need to be on a constant lookout as well as cautious to avoid missteps. Although it is interesting to witness the benefits of data science, creating a data-driven organisation and hiring a team of data science is no easy task. Not only does it require the right infrastructure but also the right mindset to embrace it on all levels.
While some non-traditional ways have emerged to hire data scientists amid this crisis, here are a few things organisations need to consider before actually hiring one.
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Understanding The Requirement & Defining The Role
The first critical step for organisations is to understand if there is an actual need to hire a data scientist for the business. Many experts believe that data science professionals come with sought after unmatched skills; however, on the other hand, many also consider that data science tools can replace those skills right off the bat. Thus organisations must estimate their position of affording a data science professional, who in general are very expensive in nature. Instead, businesses can avail the option of cross-training their employees to create more citizen data scientists.
Once established that the business can afford data scientists, it is crucial for business leaders to identify what sort of data professional is required for the organisation. Data scientists come from various backgrounds like analytics, management, product development, consultation etc. Thus, defining the requirement of data professionals would help in identifying whether an organisation requires a data analyst, data scientist or a data engineer.
Explaining this, Mamta Rajnayak, a seasoned retail analytics expert at a leading global IT services company stated, virtual hiring is quite different from regular ones. “Explaining the role in detail to the candidates might help to a great extent as they may be sceptical of the job role at the time of joining. For example, how the role sits within the organisation along with its stability, etc.,” said Mamta.
Furthermore, many data scientists come with a variety of expertise like machine learning, computer vision or natural language processing, and without understanding these profiles, companies might end up hiring wrong professionals for their organisation. Prior to hiring data professionals, it is also crucial for companies to establish their prerequisite of skill sets, to pick the right one amid the crowd — either a generalist or a specialist.
How The Data Scientist Fits In The Business Vision
Another critical aspect that businesses need to consider before hiring data scientists is to define the business vision and the significant business problem that needs to be solved. From enhancing business strategies to improving customer experience, data science can prove to be an essential asset for amplifying the bottom line. However, using massive data to solve common bottlenecks can divert data scientists from addressing real business issues.
Thus before hiring data scientists or data analysts, companies need to define their job description according to the expertise required to solve these complex business problems. A data scientist should be able to shape up the data strategies for better business performance, and for that, they need to work as a team in the organisation instead of silos. Therefore one should look for the overall skills rather than going by the niche skills that can fill the skills gap of the organisation.
According to Lakshya Sivaramakrishnan, program lead at Women Techmakers India, “a good balance between applied skills like thriving in ambiguity, along with a good domain understanding in the problem area and of course, good data science skills like data cleaning, processing, the model building would encompass a holistic candidate for the role.”
She further added, “If it’s a small team with an intent to grow further, aspects like leading with empathy and mentoring the new members to quickly ramp up to take on tasks independently will do a great deal in moving towards growth.”
Apart from understanding the requirement and defining the correct vision to the data science candidates, companies must also aim at creating a data-driven environment to seamlessly integrate data scientists to the current workflows. Without democratising data, data scientists would be immersed in data wrangling, leaving them no time actually to work on enhancing business and customer value.
Also, as Sivaramakrishnan mentioned, “While it is easy to train current employees on completing data science tasks using APIs and existing libraries, a deeper level of understanding and academic training is required for someone who is expected to build their own models from scratch.” Thus, one should be critical in hiring data scientists amid this crisis.