Back in August 2018, a LinkedIn analysis concluded that the U.S. is facing a significant shortage of data scientists, a big change from a surplus in 2015. In January 2019, job-search firm Indeed reported that its data indicates the shortage is getting worse: while more job seekers are interested in data-science jobs, the number of job postings from employers has been rising faster than the number of interested applicants.
According to Indeed, job postings for data scientists as a share of all postings were up 29% in December 2018 compared with December 2017, while searches were only up around 14%.
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But what exactly are data scientists?
Data Scientists are those scholars or practitioners who have degrees in computer science, mathematics, statistics, or a quantitative social science, along with some training in statistical modelling, artificial intelligence, machine learning, and programming.
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Let me start with my personal example.
I earned a Ph.D in Management in Organizational Leadership with a Specialization in Information Systems and Technology from University of Phoenix in Arizona (U.S.), a Master’s Degree in Computer Science (Formal Methods of Software Engineering) and a Bachelor’s Degree in Mathematics from University of Yaoundé I back home in Cameroon.
For my doctoral dissertation proposal, I chose a quantitative research method using a field survey design and structural equation modeling (SEM) techniques to analyze data collected through a stratified random sampling of the two groups of managers. Interested readers will find further details on the research method and SEM techniques in my recent book entitled “Business-IT Strategic Alignment: A Prerequisite for Digital Transformation.”
What is a successful Data Scientist?
Many believe that a successful Data Scientist is a scholar and practitioner with a sound understanding of the business.
Here we are again…We have been debating the role of CIOs for more that 25 years as companies recognized the IT potential to deliver strategic impacts. Today, CIOs are not those executives having a more active role in deciding on the allocation process of IT resources; successful CIOs exhibit a combination of business, technology and leadership skills. Even though it seems difficult to find a CIO who possesses such competencies, an excellent CIO needs to balance these different and disparate skills.
Much has been written about the specific challenges faced by CIOs, describing the characteristics needed to be a business-focused executive leading a technology-intensive function!
Back to our example again. I am member of the Association of Certified Anti-Money Laundering Specialists (ACAMS). For over 21 years, I proposed advanced evidence-based tools on financial crime intelligence and investigations, tax evasions, corruption prevention and enforcement, and organised crime to government institutions, private sectors and multinational organisations.
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With such a practitioner background and scholarly competencies as described above, can I consider myself as an excellent Data Scientist? Not really, unless I understand my responsibility in leveraging data to help other people (or machines) making more informed decisions.
The spectrum of data scientist roles is so broad that I will keep this discussion for my next post. What I really want to focus is on what are the distinctive characteristics of a great data scientist. In this era of Data Science, Big Data and AI, proposing advanced evidence-based tools in any domain areas require a sound understanding of the field.
Successful Data Scientists are therefore business-focused executive leading a data science-intensive function (see diagrams below):
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