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A calculator was once a person. Webmaster was once a hot career. Mid-level managers once had secretaries.
In each case, advancements in hardware and software took specialized
skills and put them into the hands of generalists. While specialist jobs
were lost, the democratization of these technologies unleashed waves of
innovation, commerce and job creation.
Similarly, I believe the job of data scientist as we know it today
will be barely recognizable in five to 10 years. Instead, end users in
all manner of economic sectors will work with data science software the
way non-technical people work with Excel today. In fact, those data
science tools might be just another tab in Excel 2029.
Financial analysts today rarely need to recruit data scientists to
help them because the platforms they use already provide the data
science tools they need. This will become common across many other
fields, as a basic understanding of data science will become a required
skill for many jobs. Meanwhile, much of today’s data science work is
being automated, and some observers warn that data scientists might be automating themselves out of a job.
Data Science's Soaring Popularity
Data science careers are experiencing a gold rush moment. A 2018 Bloomberg article hailed data science as “America’s Hottest Job,”
citing a 75% increase in data scientist job postings on recruiting
website Indeed.com from January 2015 to January 2018. Data science
doctorates at some consulting firms are drawing salaries of $300,000,
the article noted.
Meanwhile, dozens of U.S. universities have launched data analytics programs. UC Berkeley added a new data science major in 2018, and it quickly became one of the school’s most popular majors. In November, the university launched its new Division of Data Science and Information in what it said was its "biggest reorganization in several decades."
However, all these young people are going into a profession that may
be unrecognizable a decade from now. While their data science skills
will be a strong career asset, a surprisingly small proportion of them
will likely to be working as straight data scientists.
From Machine Code To Mass Coding To Data Automation
When I studied computer science back in the way-back-when, compiler
design was a required course. We needed to know how to convert
programming languages like C directly into machine language, the
hexadecimal code that computers interpret directly. It was common to
write pieces of commercial applications in machine language for faster
performance.
Over the past few decades, successive layers of software functions
have been abstracted into higher-level development tools. Most coding
today is done in high-level, easy-to-learn languages like Python, and
relatively few programmers need to know how to speak directly to the
hardware.
Data science is quickly following the same progression. Over the next
three to five years, higher-level tools will increasingly alleviate the
need for expertise in foundational technologies like high-performance
computing (dividing a problem across CPUs), data munging (preparing raw
data for analysis), the internals of machine-learning systems or
low-level statistical methodologies. All this will be handled under the
hood.
Today, dozens of companies -- including Trifacta, Element Analytics and Kylo
-- are introducing new data analytics tools, with many of them aimed at
reducing tedious data preparation work and allowing data scientists to
quickly get to the analytical work. Also emerging are data science
frameworks that automate algorithm selection and parameter tuning (e.g.,
Auto-sklearn, DataRobot).
These frameworks and tools are combined with platforms for data
management to create large building blocks for the data consumer of the
future.
The Path Forward For Data Scientists
Over the coming years, I foresee data scientists dividing into at least five types of workers:
1. Generalists: The first group will be data science
generalists, who will interpret data and make it usable. These
generalists will focus on educating end users, helping users ask
questions of the data rather than finding all the answers themselves.
This will likely be a transitional role, more common in five years than
in ten.
2. Industry specialists: The second and largest group will
comprise industry specialists, who will apply data science techniques
and tools in specific verticals like manufacturing, medical sciences and
finance. This is where I believe the bulk of the jobs will be. However,
these won’t be considered data science jobs. This worker won’t be a
data scientist who understands manufacturing but rather a manufacturing
leader who understands data science. Today’s equivalent is the
researcher who is a statistics ace.
3. Deep specialists: The third and smallest group will be deep
specialists in specific data science technologies. This is where the
remaining pure data science jobs will be. Their role will be pursuing
data science in the abstract, improving the performance of algorithms
and designing new generalized approaches. They will be like today’s
computer scientists, building theoretical foundations rather than
solving everyday problems.
4. Analytics developers: The fourth group will transition from
data scientist into analytics developer. These are software development
specialists who deal with data interaction and helping people make
inferences from data reports. Algorithm design will be a small part of
their job, assisted by data platforms as well as by robust code
libraries that do a lot of the work in turn-key fashion.
5. Data engineers: Finally, new jobs will emerge like the data
engineer, who builds pipelines that transform and deliver data into
foundational platforms, where the analytics and visualization take
place. While data scientists are usually recognized for their brilliant
algorithms, up to 80% of a data scientist's time could be spent collecting, cleaning and organizing data.
Conclusion
Within 10 years, data science will be so enmeshed within
industry-specific applications and broad productivity tools that we may
no longer think of it is a hot career. Just as generations of math and
statistics students have gone on to fill all manner of roles in business
and academia without thinking of themselves as mathematicians or
statisticians, the newly minted data scientist grads will be tomorrow’s
manufacturing engineers, marketing leaders and medical researchers.
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