Understanding what will draw a data scientist to a company is important when hiring the right talent.
As companies reopen offices and resume work, major restructuring exercises may be on the cards for many. From streamlining certain operations to exploring new ways of conducting business functions, companies are increasingly relying on their analytics capabilities to help them make critical decisions.
This suggests that the role of data scientists has become even more imperative amid the Covid-19 pandemic. Thus, companies should not only focus on hiring the right candidate, but also ensure that competitors do not poach their hard-won talent.
This has proved to be a challenge for many, with attrition being a serious concern for India’s data science industry as per a study conducted by AIM. In fact, the average employee attrition stood at a whopping 24.4% just a few years ago, with employees largely leaving organisations after just 3.7 years on an average.
How can companies overcome this problem? A good place to start would be to approach recruiting keeping in mind the growth of the candidate within the organisation as well. Does the company provide a learning environment where the data scientist can thrive in? Does the company give them the creative freedom to explore their talent?
Understanding what will draw a data scientist to a company is important when hiring the right talent. Here are some of the things to keep in mind in order to attract and retain the right talent:
Ample Opportunities For Growth
A thriving work environment that gives data scientists the time and space to think and work creatively should be complemented with ample opportunities for growth. On joining, candidates will expect to play a crucial role in solving complex business problems that will challenge the education and experience they bring.
In order for them to stay motivated and engaged, the company needs to lay out a clear path for career progression. Rather than spending their time doing repetitive tasks, or functions that serve little by way of learning for them, they should be made to perform jobs that will provide constant stimulation. Furthermore, their performance should be routinely assessed and accordingly rewarded with forward-looking projects that will not only enable them to push their boundaries but also make them feel appreciated and included in the company’s success story.
Additionally, companies should also introduce flexible policies that allow high-performing employees to take a sabbatical to pursue higher education, take up training programs, etc.
Involvement In Key Company Operations
One of the most important things for data scientists is to understand how their daily work impacts the larger organisation. By giving them greater ownership of the projects they handle, they will be better placed to understand the real problems faced by the company and ways to create the impact they seek.
This will also help them cultivate a broader business mindset, which will, in turn, strengthen their analysis. By enabling them to fit data analytics into the larger narrative of the organisation, and even internalise the financial implications of their work, they will be able to develop and improve their business acumen.
Investment In Upskilling
Training data scientists to help them assume different kinds of roles should be on every organisation’s agenda. And there are several ways in which companies can invest in their employee’s education.
This includes setting up additional training classes, drawing up a budget for upskilling initiatives taken up by them in their own capacity, hosting meet-ups with data science experts, researchers and academics, and more. Combined with clarity around career progression, it will send a message that the company truly cares about its employees, keeping them happy and engaged.
This is critical because most data scientists continuously look to build their skill sets to keep up with the dynamic and ever-evolving market. Thus, companies must make an effort to understand what their data science employees are learning, how they are accomplishing that, and if the organisation can support them in this endeavour.
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Access To Right Tools
Giving employees the freedom to explore different avenues to solve complex problems must be complemented with unrestricted access to adequate tools. Given the complexity of some of the problems they are likely to face, if the organisation does not provide access to these critical tools, it may keep them from effectively providing value.
Thus, before onboarding data scientists, organisations must answer some crucial questions: are existing tools appropriate for the job they are being hired for? Are they nimble and fast enough? If the answer is no, you already know why employees from your data science team have shorter tenures.
Tap Into Right Talent Network
Rather than look for sophisticated educational qualifications and a string of PhDs in data science candidates, companies should explore non-traditional methods of hiring that may be a better indicator of the key qualities a data scientist should possess.
This includes curiosity, innovative thinking, a dogged determination to solve problems, and the drive to continuously learn. Focusing on these qualities by looking at their project experiences on platforms like Kaggle, as well as their past experiences, can help create a more diverse and successful team.
One may also be surprised to find the kind of talent that exists in the startup industry today. Many talented data scientists work for smaller companies because of the flexibility it affords and the speed at which decisions are made. Another way to find a candidate who could be a good cultural fit for the organisation is to ensure that data science managers are in close contact with all potential hires since they clearly understand what the company needs.
Companies that struggle to retain good data science talent must first understand that the hiring exercise should provide value for both the company as well as the candidate. If they cannot provide the right environment for them to grow and thrive in, employees are likely to leave the company sooner than expected.