How to grow and retain your data scientist pool
Pubished 1st October 2019
The IDC’s “Data Age 2025” whitepaper predicts the global volume of data generated will increase tenfold by 2025, with 60% of data produced and managed by organisations. It therefore comes as no surprise that according to IBM, by the end of 2020, the demand for data scientists is said to increase by 39%. After all, these organisations need to employ those who have the knowledge and skills to analyse the stories this data tells, spot trends and advise firms on how to proceed. But what is the best strategy for businesses to employ in order to attract and retain the best data scientists around?
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Organisations must not underestimate the culture within their business when it comes to the growth and retention of a data scientist talent pool. Data scientists are highly paid individuals that use their data and analytics skills to find stories and contribute in innovative ways to business outcomes. Businesses therefore need to create a welcoming culture that details from the outset the overall vision for the work undertaken, and how this work is paramount to make a difference.
This company culture extends to the ecosystem of the organisation. Firms often hire data scientists who work independently from other teams. As a result these individuals have no attachment to the rest of the company. The result of this is creating an unappreciated atmosphere, where data scientists’ work isn’t valued as it isn’t understood. Creativity can also stifle, as ideas presented are rejected for not fitting in with the rest of the business.
During a recent data scientist roundtable hosted by InterQuest Group, lack of implementation was seen as a key factor of frustration. The data scientists present at the event stated that in the majority of cases, credibility and praise is provided once a system has been implemented. However, in a number of scenarios, work conducted by these individuals never reached the stage in which a business was ready to implement.
There were a number of further findings from this roundtable. Older data scientists described how once university was completed, the aim was to land a role within the big 4 and have a career for life. However, nowadays smaller less known organisations also possess large, desirable data sets. Data scientists therefore no longer need to compete for careers at large organisations. As a result, increased choice has allowed data scientists to move at will.
Or does it come down to the fact there is a larger demand for data scientists, providing them the power to move much easier if they don’t like their role?
And if the market tightened, would there be an influx of data scientists applying for more stable roles at the big 4 again?
Hiring and retaining data scientists will only increase in importance as machine learning and artificial intelligence continues to weave itself within society at rapid pace. Organisations that do not empower and engage their data scientists risk a retention crisis as top talent moves on. In order for organisations to make optimal use of the talent at their disposal, a clear plan is needed to ensure data scientists have the correct culture, team and work to achieve their goals, coupled with a competitive benchmarked salary.