Just a few years ago, the term “Data Scientist” encompassed everything from statisticians to Software Engineers. As the field has evolved so has the definition and the hiring process of a Data Scientist. At Xcede we often speak to clients about how they can retain their Data Scientists and keep their position at the forefront of innovation.
Our Data Science Collective Roundtable on the 18th of January discussed best practice when hiring Data Scientists with Heads of Data Science from Criteo, The Economist, Farfetch, Yoox Net-A-Porter, Inmarsat, Profusion, Barclays and the FSA. The questions posed helped us to look deeper into how we can improve the hiring process for a Data Science position.
What was Discussed?
The discussion initially revolved around the design of the job description and the hiring process itself. As the conversation evolved, it became clear that one of the biggest issues is that there is a gap between the expectations of what a Data Scientist job roles should be and what it in fact is. This is somewhat alarming as it effects both involved - Data Scientists and companies and also the future of the hiring process.
The role of a Scientist is often viewed as one of the most attractive and principally involves a lot of essential work which can be deemed as quite rudimentary and lacking new and innovative algorithms that a Data Scientist might hope for. Due to these tainted expectations, many Data Mangers say that their experience jaded employees rather quickly. One of the reasons behind this was thought to be the gap between the job description given in interviews and in adverts in comparison to what the job actually consists of on a day-to-day basis.
Following this, another aspect as to why some Scientists become uninterested in their position after 1-2 years’ is the nature of many Data Scientists - they are often curious and keen to learn. The Roundtable concluded that the key to retaining talent and keeping the role interesting was to feed this curiosity and continuously offer possibilities to develop within the position and their organisation. This must be implemented from an internal perspective, marrying together values and capabilities to design the optimum structure that best suits their organisation.
Skillsets and Experience
In addition to companies bringing value to the role through learning, many of the Managers pointed out important qualities of a Data Scientist that necessarily don’t correlate with the skill set they have, or even previous experience. Instead, important qualities of a Data Scientist include their ability to deliver simple solutions that are just as effective, open to new ideas and finally to remain modest.
It is important to improve opportunities available to Data Scientists, this includes taking part in projects and to remain involved in developments that will be particularly rewarding. Motivation and knowledge are key aspects of a Data Scientists position, by investing into these areas our attendees believe that this will improve working environment and will increase retainment levels for these extremely sought after specialists.
The key points of the evening were focused on how to better mirror the responsibilities of the role to the job description. This extra investment in time communicating and exploring the role in further detail are beneficial for a Scientists long-term perspective. By doing so, managers can control expectations and ultimately manage their Data Scientists in a way that not only encourages but innovates and facilitates their working environment. Subsequently, the discussion to hire a Data Scientist must be followed up with a streamlined and honest hiring process to encourage and nurture future projects and talent.
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