Back in 2012, the Harvard Business Review hailed Data Scientist as the ‘Sexiest Job Title of the 21st Century’, but by no means is Data Scientist the sexiest job of the 21st Century.
Data Scientist has now smashed the Glassdoor Best Job in America ranking for two years in a row too, which takes into consideration not only salary figures but data from the website’s user reviews, job adverts, and job satisfaction ratings. The job role has seen a rise from 1,736 job openings in Data Science recorded on Glassdoor in 2016’s ranking to 4,184, highlighting the growing demand for this expertise.
But, being a Data Scientist is much more than a glamourous job title and generous salary. It takes serious commitment to become a great Data Science practitioner in this competitive, candidate driven market we’re seeing grow exponentially today.
Studying further degrees are extremely time-consuming and take a huge commitment. Courses and PhD research for 5 or 6 years are a stressful time for Data Scientists. To then go into the commercial sector, the expectations here can be much more demanding. In a commercial environment, pressure can come from a variety of sources – time, colleagues, money, answers, the list goes on. Most organisations will expect some quick results so picking the projects with low hanging fruit becomes important.
This can be daunting for a rookie Data Scientist, so some guidance from the wider Data Science team could be key to initial success. Whereas in a research environment, the pressures - whilst still demanding - are perhaps not as pointed.
Data plays a huge part in business decision-making and the skills required to manage these data sets fall well outside of the remit of managers and executives. This means a lot of pressure can be felt by Data Scientists who are working for companies with shareholders expecting to see profit and business input directly from your insights; how business owners can mitigate huge risks and significantly boost bottom line profits.
It’s a hard task having a labour of love for years! Increasing an algorithms accuracy by 1% can take serious hard work, time and energy. Not only are daily tasks impactful, but to meet expectations you should be prepared to work difficult or unsociable hours and over our time, we’ve definitely seen that it can test people on a personal level. Cleansing the data in order to be able to draw insight from it can be one of the most time consuming and laborious tasks of a Data Scientist, but it’s often the key to success.
This is all not to say it’s not one of the most rewarding careers out there. Data Scientist’s can expect great job satisfaction with the chance to work on projects that have a massive societal impact; from designing deep learning algorithms to diagnose cancer, autopilot cars and drones, to control robots. The growing need for Data Scientists in such a wide range of industries has allowed a lot of scientific researchers the chance to move into careers that don’t jeopardize or mute their love of research.
It’s such a broad job role that allows for experimentation and research into things that have never been done before, especially within the diverse start-up scene that is filling up with innovative and disruptive cutting edge tech companies with heavy investment.
The role of Data Scientist has also become a lot more complex. Especially within the field of Artificial Intelligence, Data Science is extremely important; as with the development of self-creating AI software, lower tier tasks are falling subject to automation as Data Scientist’s progress toward more complex duties. The Data Scientist of today needs to be adaptable as the role evolves at the rapid speeds of technological development. Just make sure you’re prepared!