The future of data science jobs
Data scientists are one of the most sought-after roles in corporate America today, as organizations with the right talents can derive more value from their data.
However, the roles of data scientists are changing due to technological innovation and market maturity. In fact, the titles of statistician, actuary and quant, depending on the industry, preceded the title of data scientist.
However, there are challenges when it comes to determining how the role of the data scientist evolves. On the one hand, there are no clear requirements for the position of data scientist.
What is data science?
Data science, as defined by industry professionals today, is the study and use of data to inform business decisions and create new products for customers. Data scientists are typically responsible for analyzing data to find new information. They often use advanced machine learning models to predict future customer or market behavior based on past trends.
The ultimate goal of what companies hope to get from data scientists shouldn’t change. But the way data scientists achieve these goals is likely to undergo substantial changes in the years to come.
Diplomas and Qualifications for Data Scientists
Many senior data scientists typically have advanced degrees in math or statistics and are proficient in problem solving. Others have a background in computer science, astrophysics or other subjects.
“Do I believe data scientists need to have these specific degrees? No, absolutely not,” said Kathleen Featheringham, director of artificial intelligence strategy and training at the management consulting firm and in computer technology Booz Allen Hamilton. “There have been a lot of definitions, but inherently someone who is curious.”
Like any other role, a data scientist can evolve into something else, and there are a few metrics that will happen.
Kathleen featheringhamDirector of AI Strategy and Training, Booz Allen Hamilton
Does data science have a future?
Experts said that 80% or more of a data scientist’s job is to prepare the data for analysis. Now, technology vendors are selling platforms that automate tasks and abstract data in low-code or no-code environments, potentially eliminating much of the work currently done by data scientists.
“[The data scientist title] is probably going to disappear in the background because more and more tools are more and more prevalent, “said Featheringham.” To me it’s like designing websites years ago when you had to have people who really love code, but now you can go online and use a build your website for you. “
How will quantum computing impact data science jobs?
Quantum computing and quantum information science are still in their infancy, but they represent a new market for data scientists.
“If you do a calculation on a classical computer and you have a bunch of initial inputs, you have to run them one by one. On a quantum computer, you can run them all at the same time,” said Patty Lee, a scientist in data manager at Honeywell Quantum Solutions.
“You can’t just take a classical computational algorithm and plug it into a quantum computer. You have to come up with new algorithms that take advantage of the properties of quantum mechanics, and then you can extract the information from your data that way, ”she mentioned.
Quantum data scientists need to understand quantum mechanics and how to use a quantum algorithm to solve a particular problem. However, Lee doesn’t think they necessarily need a graduate degree in the subject.
“We need a lot of people in this space because there are people on the business applications side and quantum theorists who are familiar with quantum algorithms. We need someone in the middle to do the translation.” , Lee said.
Data Scientist vs Data Engineer Jobs
In today’s world, a business is better off having the right mix of skills rather than the right mix of titles.
Yet titles help individuals and others understand the scope of their responsibilities and their salary range. Even people who have achieved the coveted title of Data Scientist can move on to another role because it suits them better or because their business needs something else.
While a data engineer is more likely to become a data scientist in the United States, the opposite trend is occurring in the United Kingdom, according to Rob Weston, founder of Heimdal Satellite Technologies.
“They’re expected to run only on machine learning, which they absolutely don’t. How to prepare the data? How will the data be transferred to the pipeline? ” Weston said. “The challenge is that the volume and diversity of data change and therefore the ability to manage and move data is an engineering problem.”
Many organizations think they need a data scientist, but that may not be the case. Recruitment company ManpowerGroup is aware of this phenomenon, so they first ask clients what business problem they are trying to solve.
“A lot of people hear buzzwords and they want those buzzwords, but it’s not really what they need,” said Chuck Kincaid, senior data scientist and product architect at Experis Solutions, an affiliate. by ManpowerGroup.
Kincaid said one of his biggest concerns now is applicants listing software tools on their CVs that they don’t know how to use properly. Likewise, he warns against candidates who try to fully appropriate the merit of a group project.
Basic Qualifications for Data Scientists
The Data Science Association, a nonprofit trade association of data scientists, wants to set standards for certifications and licensing in data science. From a career perspective, this would mean that data scientists would have to meet predefined criteria to apply for a license and anyone who is not a licensed professional could not use the title legally.
Weston makes a point of verifying a candidate’s qualifications and is often disappointed. For example, if he gives a candidate a hypothetical scenario, the “49 out of 50” candidates will say that they have never worked in the industry in which the hypothetical scenario takes place, rather than demonstrating their prowess to solve. problems and offer an answer.
“I recently interviewed a guy who had a massive CV that said data science, big data and a lot of roles in all the fields that we are looking for. We need very sophisticated analytics because we are dealing with petabyte order data. Said Weston. “I said,” We use Python for most of our code. How can we use Python in EMR Spark? What libraries could we use? “He couldn’t answer the question and had never even heard of PySpark. That’s a legitimate question since his CV indicated three years of experience in this field.”
Ultimately, the role of the data scientist is changing, although exactly how it changes is a matter of debate. Automation speeds up and simplifies some tasks, but it does not yet automate data scientists. Meanwhile, other opportunities are emerging, such as quantum data science.
Will the role of data scientist end up disappearing? Some believe it will. However, in the meantime, there are plenty of opportunities for those who have mastered their craft.