According to the Harvard Business Review, data science is the hottest job of the 21st century. This is undoubtedly because "data is the new oil"; a phrase coined by Clive Humby after he used it to double the revenues of the Tesco supermarket chain. Tesco’s chairman is reported to have said: “What scares me about this is that you know more about my customers after three months than I know after 30 years."
That sums up the power of Big Data. Since then, data analytics has found applications in fields as diverse as telecommunications, healthcare, network security, insurance, finance, weather prediction and policing. We have only begun to discover all of its potential.
After years of steady growth, a survey by IBM predicted there would be 2,720,000 openings for data scientists in 2020. Coronavirus may have dented that prediction slightly, but as the economy picks back up, it will soon be an underestimate.
Competing digitally is now more important than ever, and that means there is a bigger post-lockdown role for artificial intelligence, machine learning, automation, predictive intelligence and personalized customer experience. That should be good news for data scientists, analysts and database managers - if you can find them.
There are new jobs in data science every day, but most of them are still vacant the following day - and the following month. Some are never filled, and the projects for which they were needed never come to fruition.
The dearth of specialized data scientists also inflates salaries. Many personnel enjoy salaries approaching $100,000, and some earn significantly more; chief data officer salaries often approach $180,000. As a result, even when companies successfully locate eligible talent, they often decide they cannot afford to build the team they wanted. An even more unfortunate turn of events is to make the investment, begin a valuable project and then fail to retain key talent.
Around one third of the vacancies for data scientists specifically ask for higher degrees, compared to around 6% in related roles like data analysts and system developers. Healthcare and retail sectors often struggle the most to fill vacancies, either because funds are tight or because candidates cannot see future prospects beyond the initial project.
Building and retaining your team
Throwing money at the problem is not the way to build a strong and loyal data science team. When you are first venturing into data science, it is often best to hire those with broad rather than specialized skills and supplement your in-house team with MLaaS and analytics products from Cloud platforms like Azure and AWS. Hone and expand your team later as your objectives and requirements become more detailed and ambitious. Building a winning data science team is often more about assembling the right mix of people at the beginning.
Tsvi Gal, CTO at Morgan Stanley, calls it a myth that data analytics is mechanical rather than creative work. He says “It’s not just that you run an algorithm and you follow it…. you need to look for patterns in the data that may or may not exist." Consequently, Morgan Stanley employs a significant number of people with arts backgrounds because they “can think in a different and more abstract way."
Employers are prone to overestimating the formal experience some team members will need. Statisticians do not always need to be strong coders. Data visualizers don’t necessarily need to be skilled analysts. Looking for people who are perfect in every facet of data science could mean a long wait and may end in disappointment. Understanding the information potential inside data is an aptitude rather than a qualification, and many of the required skills are transferable from other IT sectors.
Building a team, however, is a skill in itself. TheDataLogic team specializes in precisely this skill. We build lasting relationships with our candidates so we can provide the career path, prospects and stimulating challenges that engender their loyalty and creativity. Let our data science specialists build the perfect team for you. Please contact us now using the details below.
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