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This article was contributed by Michael Burke, head of the PR Intelligence Lab at BYU.
Digitization accelerated around the world as the pandemic sent many people in and online, and this digitization continues for the data science industry. As society has changed, the increased demands must be met at a rapid pace. Customers are online more than ever, leading to a massive increase in data. Now, data science is one of the top careers for recent college graduates as the need to make strategic, data-driven decisions has increased at exponential rates across industries.
Industry and company-wide commitments to data science and digital transformation are not small – in fact, it is practically a gold rush for talent. Look at the Fortune 250 fintech, FIS, as a prime example. In the past year, they have pledged $150 million to innovation ventures, built a complete real-time payments engine to instantly move B2B transactions internally, and launched an Impact Labs incubator in Denver, which has already It’s got a product called GoCart, which replaces the shopping cart. Experience in one-click payments.
Finance, health care, and other big “old” industries are generally known for being the fastest to make digital transformation, so when you see these kinds of moves, you know data science is making an impact. .
And that effect will only continue. Data is the backbone of businesses across industries, and some projections indicate that the total amount of data created, captured and consumed will reach 149 zettabytes by 2024. These staggering numbers prove not only how much the sector will grow but also why it is so important to identify trends and stay ahead of them. Two big trends right now are with people getting into the data science field, and the conflict in the types of data that companies big and small are prioritizing.
Applicants in the data science industry
We are seeing an increase in the number of people who went to school with a math background (or who may have started out in data engineering) who want to switch to data science. This should be great news for anyone seeking a degree in math (or a STEM-related field) and now looking to make a career change to be a part of this fast-growing industry. This can also be good news in the sense that people with a solid understanding of how to interpret data properly will be available. Although software advances may allow users to create charts more easily, they will not necessarily be able to understand all of their nuances and implications. More mathematicians in data science means more grounded decision making.
“10 years ago, you’d have to go down a very specific track and make a structured career decision to end data science,” says Michael Tarcelli, TetraScience’s chief scientific officer. “Nowadays, scientists are coming out of school and saying, ‘You know what? I can end up on this. I can do a month’s worth of data science boot camp and learn more about Python, recursive logic, or neural networks. I can school myself quickly and then boom, they are a prime candidate for us.
We’re also seeing a lot of applicants who have a data science education or a data science-friendly education, but who have moved on to other fields. The main reason for this is that when these job seekers entered the market, the data science field was not flourishing the way it is now. This means we have data scientists with vast experience who are only now joining the industry, and the value of this domain expertise cannot be underestimated. To interpret data relating to a fintech company, you have to understand the language of finance – and this knowledge is not something one acquires overnight. Fortunately, with the right educational background or certifications (which are now more accessible than ever), many people who have a wealth of domain-specific expertise can consider turning a career into data science.
data science divide
The other big trend is we’re looking for big companies in data scientists and what smaller companies and startups are looking for—and that divide is growing.
Big companies already have a lot of infrastructure to manage their data and clean it up. They are looking for data scientists and researchers to come in with a focused and narrow scope and go much deeper. Big companies are looking for scientists to focus all their attention on specific data science problems.
Startups and smaller companies, on the other hand, are likely to lack the data infrastructure and have to determine how to put one in place and then use the data it pulls in. They are looking for a “jack of all trades” who can begin to gain insight into production and work on more stacks.
As many people starting out their careers need to consider what type (and size) of company they want to work for. Smaller companies give you room to grow and focus on broader issues, and larger companies stay more focused and specified. This gap continues to widen even today.
Relationship trends between company and job candidate
As the field continues to grow at a rapid pace, the recruitment process becomes more competitive on both sides of the spectrum. Companies have a good number of people to choose from, and applicants know that companies need data scientists now more than ever. The hiring process depends on where any company is on its data maturity journey.
Smaller companies are looking for people with more chops who can approach things from a full-stack perspective. Large companies are looking for specialized knowledge and academic machine learning researchers to build comprehensive models. But no matter where they are in that continuum, companies and job seekers alike are trying to find the right fit. Early in their career they may be seeking mentorship, growth, and an understanding of how data science fits into real-world scenarios. Whereas, those who are more senior in their positions want to focus on difficult but achievable problems.
One thing you might hear from a senior data scientist is that they were brought in to do data science but the company wasn’t ready for it, so they were saddled with other responsibilities (like data engineering). These data scientists will be brought in to work on projects that sound interesting – but eventually become a letdown when the company reveals they are not ready to execute.
Some of this may be due to deficiencies in the recruitment process. Since most human resources departments lack the experience of hiring data scientists, they often fall into the trap of testing applicants for overly broad job descriptions and non-relevant skills. Whatever the reason, it leaves data scientists at an interesting place in their careers (especially if they are unable to do the work they set out to do), and it motivates them to seek new opportunities. Can do. Overall, the trend favors data science experts over generalists, so data science professionals should consider how they can consolidate and specify their strengths to excel.
In general, every business leader should aim to unlock human potential – and this is especially true in areas that rely on continuing education, innovation, and passion. Few industries are experiencing an explosion of need and innovation like this pandemic-driven industry. Businesses will continue to drive their digital and eCommerce efforts through data, and we have the opportunity to help drive the economy through our educated efforts. Glassdoor has also ranked data science as the #2 profession for 2021. As we continue to grow as an industry, people in this sector will find more and more opportunities for growth – opportunities that business leaders should strive to enable.
Michael Burke is a Data Scientist with an MS in Data Analytics. He has written extensively on machine learning-related topics, and has won industry awards for his studies of factors affecting SEO. He leads the PR Intelligence Lab at BYU’s School of Communications.
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