The Future of Data Science Jobs

The designation " Data Scientist " may soon be outdated as new technologies change the tasks of data scientists. The nature of the job may also soon change.

Data scientists are one of the most in-demand positions in American companies today because with the right talent, companies can get more value from their data.

However, the role of the data scientist is evolving as technology innovation and market maturity. In fact, depending on the industry, the titles Statistician, Actuary and Quant were precursors to the title Data Scientist.

However, there are some challenges in determining how the role of the data scientist is changing. On the one hand, there are no clear requirements for the job of data scientist.

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What is data science?

As defined by today's industry experts, data science is the study and use of data to make business decisions and develop new, customer-focused products. Data scientists are typically responsible for analyzing data to gain new insights. They often work with advanced machine learning models to predict future customer or market behavior based on past trends.

The goal that companies hope to achieve from data scientists is unlikely to change. But the way data scientists achieve these goals is likely to change significantly in the coming years.

Data Scientist Degrees and Qualifications

Many of the best data scientists have graduate degrees in mathematics or statistics and are masters at solving problems. Others have a background in computer science, astrophysics or other subjects.

"Do I think data scientists need to have these specific degrees? No, absolutely not," says Kathleen Featheringham, director of AI strategy and training at management and IT technology consulting firm Booz Allen Hamilton. “There are a lot of definitions, but it’s someone who is naturally curious.

Like any other role, a data scientist can evolve into something else, and there are some signs that this will happen.

Does data science have a future?

Experts have said that 80% or more of a data scientist's job is preparing data for analysis. Meanwhile, technology providers are selling platforms that automate tasks and abstract data in low-code or no-code environments, which could eliminate much of the work currently done by data scientists.

“The term data scientist will likely fade into the background as more tools become more common,” says Featheringham. "To me it's like website design years ago when you needed people who really liked coding, but now you can go online and use a tool that will create the website for you.

How will quantum computing impact data science jobs?
Quantum computing and quantum computing are still in their infancy, but they represent a new market for data scientists.

"If you're doing a calculation on a classical computer and you have a set of initial inputs, you have to run them sequentially. On a quantum computer, you can run them all at the same time," says Patty Lee, chief data scientist at Honeywell Quantum Solutions.

"You can't just take a classical computing algorithm and plug it into a quantum computer. You have to develop new algorithms that take advantage of the quantum mechanical properties, and then you can extract the information from the data that way," she said.

Quantum data scientists need to understand quantum mechanics and how to use a quantum algorithm to solve a specific problem. However, Lee doesn't think they necessarily need a college degree in the field.

"We need a lot of people in this area because there are people on the application side of companies and quantum theorists who know quantum algorithms. We need someone in the middle to do the translation," Lee said.

Jobs for Data Scientists vs. Data Engineers

In today's world, it is better for a company to have the right mix of skills than the right mix of titles.

Still, titles help individuals and others understand the scope of their responsibilities and their pay scale. Even people who have achieved the coveted title of data scientist may grow into a different role because it suits them better or their company needs something different.

While a data engineer is more likely to become a data scientist in the US, the opposite trend is seen in the UK, according to Rob Weston, founder of Heimdal Satellite Technologies.

"There's an expectation that they're just doing machine learning, which is absolutely not the case. How do I get the data ready? How do I get the data into the pipeline?" Weston said. "The challenge is that the volume and variety of data is changing, and therefore the ability to process and move data is a technical problem."

Many companies believe they need a data scientist, but that's not necessarily the case. The personnel service provider ManpowerGroup is aware of this phenomenon and therefore first asks its customers what business problem they are trying to solve.

"A lot of people hear buzzwords and want those buzzwords, but it's not really what they need"

says Chuck Kincaid, a senior data scientist and product architect at Experis Solutions, a subsidiary of ManpowerGroup.

Kincaid said one of his biggest concerns is applicants listing software tools on their resume that they don't know how to properly use. He also warns against applicants who try to get full credit for a group project.

Essential qualifications for data scientists

The Data Science Association, a nonprofit professional association of data scientists, seeks to set standards for certifications and licensing for data scientists. From a professional perspective, this would mean that data scientists must meet predefined criteria to apply for a license, and anyone who does not have a license cannot legally use the title.

Weston makes it a point to check applicants' qualifications and is often disappointed. For example, when presenting a candidate with a hypothetical scenario, "49 out of 50" candidates say they have never worked in the industry in which the hypothetical scenario takes place, rather than demonstrating their problem-solving skills and coming up with an answer.

"I recently had an interview with a candidate who had an extensive resume that included data science, big data, and many roles in all the areas we're looking for. We need advanced analytics because we're dealing with petabytes of data. area," Weston said. "I said, 'We use Python for most of our code. How can we use Python in EMR Spark? He couldn't answer that question and had never heard of PySpark. A fair question because his resume said that he had three years of experience in exactly this area."

Ultimately, the role of the data scientist is changing, although exactly how the role is changing is debatable. Automation speeds up and simplifies some tasks, but it doesn't put data scientists out of work. In the meantime, new possibilities are emerging, such as: B. quantum data science.

Will the job of data scientist eventually disappear? Some believe this will be the case. In the meantime, however, there are plenty of options for those who have mastered their craft.