Data Science Jobs | The Must-Read Articles9 min read
In an increasingly data-driven world, more and more data science jobs are being created.
Qualified, talented data scientists are increasingly in demand.
6.5 times as many data scientist positions were posted on LinkedIn in 2018 than in 2012.
It seems as if there are too many data science jobs and not enough candidates to fill them.
But is this the case? Or is the problem that there aren’t enough qualified candidates for the positions? Or that competition for the jobs is much higher, as the bar for candidate quality has been raised?
Either way, the ‘Data Science Gap’ is real.
But who’s responsible?
So, what’s the cause of the ‘Data Science Gap’?
Growth Tribe’s Head Science, Bernardo Nunes, believes that the problem doesn’t lie with data scientists themselves. While compiling this reading list, he came to three conclusions:
- The people responsible for the “Data Science Gap” are the Directors, HR departments and even recruiters who don’t really know what data scientists do, how data science teams are configured or what makes a great data scientist really “great”.
- We need to use more narrow, defined job titles to describe the different roles that work with data. For example, clearer distinctions need to be made between machine learning engineers, data translators and data engineers.
- Data scientists frequently fail to master data engineering. Although this isn’t their main purpose, it is crucial to be able to put the data insights into production mode.
February 2019 | Forbes
There is evidence to suggest the rise of the data scientist is a temporary phenomenon. This is a normal part of the technology hype cycle.
The coming disillusionment with data science job titles will be the following:
- Many data science teams have not delivered results that can be measured in ROI by executives.
The excitement of AI and ML has temporary led people to ignore the basic question: What does a data scientist actually do?
- For complex data engineering tasks, you need five data engineers for every one data scientist.
- Automation is coming for many tasks that data scientists perform, including machine learning.
- Every major cloud vendor has heavily invested in some type of AutoML initiative.
March 2019 | Forbes
The job of data scientist as we know it today will be barely recognisable in five to 10 years time. Instead, end users in all manner of economic sectors will work with data science software in the same way that non-technical people work with Excel today. In fact, those data science tools might be just another tab in Excel 2029.
5 different types of data professional roles are starting to appear:
- Industry Specialists
- Deep Specialists
- Analytics Developers
- Data Engineers
January 2019 | KDNuggets
The future of work is through data. Those who aren’t already working in data and analytics will soon be utilising the technology for its undeniable business benefits.
CEOs all over the world are looking for candidates who possess specific knowledge and skills, such as:
- Education in business data analytics
- Working knowledge of coding languages and programs, such as PYTHON or R
- The ability and propensity to learn new coding languages and programs
- The ability to work well with others as well as individually
- Critical thinking and problem-solving skills
- A college minor or working experience in other, tangentially related fields such as marketing, HR, cyber security, transportation, or customer service.
January 2019 | KDNuggets
University grads feel overwhelmed by the many options that there are after university. In Data Science specifically, there are many different areas to get started in or focus on, which doesn’t make it easier. This is where a career mentor could help.
Career mentors who are already working in the data science industry can help by:
- Building networks and connections in relevant industries
- Focusing your learnings on relevant topics and skills that are more likely to get you hired
September 2018 | Forbes
No industry today can say that it is not data-driven. With vast volumes, speed and varieties of data coming from external and internal sources, the need to scientifically approach data is paramount for competitive intelligence of organisations. That is what is keeping CxOs awake at odd hours.
By looking at the ‘what’ and the ‘why’ of the current state of the data industry,
It’s clear to see that there’s a huge need for expertise, insights and powerful “what-if” thinking. Businesses need to be able to use a combination of analytics and machine learning to techniques to be able to draw valuable insights out of the massive sea of data.
In total. Bernardo compiled a full list of 43 publications, articles and posts about the role of the Data Scientist in 2019.
These articles answer the following questions:
- Which data skills does your company need?
- What are the top 10 roles in AI and data science?
- How does it feel to learn data science in 2019?
- What are the top skills that aspiring data scientists are missing?
- What is a data science project flow?
Get full, free access to the complete Data Scientist Reading List here!
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