Top 5 Reasons to Become a Data Scientist

Looking to get into data science? DiscoverDataScience.org recommends it as a lucrative, in-demand, and securely growing field but as you’ll discover, all the top resources and experts are unanimous in this projection.  In fact, according to Glassdoor, data scientists have ranked as the “number one job in America” in four of the past five years, still ranking third in the last year. Harvard Business Review describes data science as “the sexiest job of the 21^st Century.” Move over rock stars and supermodels and enter data scientists!

So what is it that makes data science such a scorching hot field to get into? As an introduction to a long list of rewarding reasons, let’s focus on what research has identified as the top five:

1. What is a Data Scientist
2. Growing Demand for Data Scientists
3. Data Science Base Salaries are Hard to Beat (Plus Perks!)
4. Data, an Ever-Evolving Field
5. Diverse Career Growth Options for Data Scientists

First Things First: What is a Data Scientist?

Before we answer the question of why you should become a data scientist, it’s worth spending a little time on what exactly this career path entails. Part mathematician, part computer scientist, and part business strategist, data scientists must have expertise in several different disciplines at once. This complex skill set means that data scientists need to consistently have one foot in the information technology sector, and another planted firmly in the business world. That’s part of what places this expertise in such high demand and why becoming a data scientist is one of the best career decisions you can make.

Data science is primarily focused on deep knowledge discovery through data exploration and inference and a good data scientist must possess both the statistical knowledge and computer skills that are needed for solving complex problems. This discipline focuses on using mathematical and algorithmic techniques to solve some of the most analytically complex business problems, leveraging troves of raw data to figure out the hidden insight that lies beneath the surface.

The core of the data science field centers around precise and often minutia-driven analysis, building strong decision capabilities, and can at times be a quiet and solitary field. However, data scientists must also possess exceptional verbal, written and visual communication skills, as they will likely be called upon to express their findings and analysis to their superiors, colleagues on different teams, and company stakeholders who may or may not be able to follow complex statistical jargon. A data scientist will need to impart convey what they’ve discovered, how they know that they’ve discovered it, and what needs to be done about it now that the information is known, all in a comprehensive and easily digestible way. Not always an easy task.

On any given day, a data scientist may be extracting data from a database, preparing the data for various analyses, building and testing a statistical model, or creating reports that include easily understandable data visualizations. While data science projects and tasks may vary depending on the enterprise, there are primary job functions that tend to be common among all data science positions such as:

1. Collecting massive amounts of data and converting it to an analysis-friendly format.
2. Problem-solving business-related challenges while using data-driven techniques and tools.
3. Using a variety of programming languages, as well as programs, for data collection and analysis.
4. Having a wealth of knowledge with analytical techniques and tools.
5. Communicating findings and offering advice through effective data visualizations and comprehensive reports.
6. Identifying patterns and trends in data; providing a plan to implement improvements.
7. Predictive analytics; anticipating future demands, events, performances, trends, etcetera.
8. Contributing to data mining architectures, modeling standards, reporting and data analysis methodologies.
9. Inventing new algorithms to solve problems and build analytical tools.
10. Recommending cost-effective changes to existing procedures and strategies.

If you’re looking for more information on what a career in data science entails, Discover Data Science has amassed a detailed career guide that you can explore by area of specialization. If you’re still in the exploratory phase of pursuing a career or education in data science, we recommend weighing these top 5 reasons to get into the field.

1. Have a Huge Impact on Your Company and Your Planet

Of all the reasons to get into data science as a career, one of the most effective (at least on a global scale) might be the impact you can make as a leader in the field, both with within your company and on the world at large.

Within your own organization, you can work toward automating processes that were previously manual, saving the company time and potentially thousands or even millions of dollars, allowing them to allocate those resources elsewhere. As we’ll discuss, companies in modern areas of industry are well aware of the tremendous value that their data scientists provide and how much they depend upon them. That leaves data scientists well-positioned as a strategy guru within organizations, whether it’s a small start-up or a titan like Amazon, Apple, or Uber, all of which are always in the market for a effective data scientists.

While a lot of data scientists work in the financial sector or for Big Tech, if that’s not your thing, you will find a host of different industry for you to put your skills to use. However, as a data scientist, above all, you’re working in the field of understanding human nature, examining what we’re interested in and what we’re not and why. There’s not a business out there that doesn’t have serious need for someone like that, including fun careers in fields like professional sports, beauty and cosmetics, and the entertainment industry.

Globally, the chance to make a difference through data science is even more profound. People without much exposure may think the job entails being a glorified number cruncher. While it’s true there will be a fair amount of that in the typical day-to-day, there is so much more potential value for data science outcomes than presumed. How you impact the world as a data scientist depends a lot on where your passions lie and in what field you want to make your mark.

Passionate about fighting the devastating effects of climate change? As a data scientist you might be building more accurate climate models and weather prediction technology, helping to save lives, or you might be working on cutting-edge public transportation projects, cutting down on CO2 emissions. Looking to help fight hunger? You might work to analyze agricultural practices and crop yields to help farmers increase their food output. This can help small family farms save money and keep their business viable. Data scientists work on fighting cancer, preventing blindness, developing new drugs and medical technologies, and empowering the developing world.

2. Growing Demand for Data Scientists

Data science remains a career on the rise, consistently regarded as one of the most in-demand fields for much of the past decade, and this shows no sign of slowing down at all. In fact, the U.S. Bureau of Labor Statistics (BLS) projects a 36% growth in data science occupations through 2031. LinkedIn reported a shortage of over 151,000 data scientists across the United States in 2018, particularly in the New York City, San Francisco, and Los Angeles metro areas.

So, what is it that makes data science a career that’s in such high demand? Think of the most dominant and influential companies in the world. It’s a decent bet that you thought of industry icons like Amazon, Google, Apple, or Facebook, each of which thrives foundationally on data-driven decision making.

Amazon uses data analytics to drive sales and marketing algorithms, recommending products to customers based on past purchases and behaviors.

Google Search utilizes data to determine the ranking and SEO value of webpages, singularly driving the user experience of online properties and access to content.

Apple makes product decisions based on when and how its iPhones, iPads, Macbooks, and other devices and tech are being used by you, the customer.

Facebook uses data to show you perfectly targeted ads and translate connections between users and communities. The collection of our data informs all those decisions and more, and it’s data science professionals who are responsible for influencing those decisions.

A company almost can’t survive today without adopting a data-driven approach to their business and advancing it based on trending applications. Yet, the supply of data scientists still remains quite low, hence the demand – surplus versus shortage is still imbalanced creating both need and great opportunity in data science related industries. Even in 2021, it’s still a relatively new and emerging field.

While other 21^st century careers like web design and programming have already started to become part of the curriculum of traditional education systems, data science can’t always say the same. There is a wide gap between the worth and prevalence of data science in global industries and day-to-day community operations or “corporate America” versus the presence or understanding of data science in primary education. This is starting to change especially in undergraduate and graduate degree programs, but it hasn’t reached critical mass yet, and the need for data scientists still seems to be greater than the number of experienced data scientists that are currently on the market.

All this may make the prospect of becoming a data scientist seem a little daunting, but on the contrary, students or prospective job candidates who pursue this unquestionably advantageous field are solidifying their status as sought-after contributors and “experts” in a top contemporary workforce that shows zero sign of attrition.

Whether new to data or committed to investing the work that places yourself in the lead in a leading industry, successes in data science are significantly attainable. A bachelor’s degree (or better yet a Master’s degree or PhD) in in fields like mathematics, statistics, computer science, or engineering is a place to start, along with experience in SQL and machine learning. You may need to start in an entry-level field as a data analyst, but with a proficiency as in-demand as data science, you’ll get your chance to make your move, and once you’ve shown you can do the job, you’ll have your pick of top companies and top positions in the profession.

3. Data Science Base Salaries are Hard to Beat (Plus Perks!)

When you are in an emerging and such high-demand field as data science with such a scarcity of qualified candidates, that’s naturally going to a merit an extremely competitive salary base and growth window. Data science is no exception. According to the BLS, the median salary for a data scientist is $100,910 a year as of May 2021. While that may be a high enough number already, the BLS also says the top 10% of data scientists can earn upwards of $167,040 annually.

Keep in mind that these numbers are always changing and can vary greatly depending on where you live and what kind of company you work for, so we encourage continued current research about the career’s outlook in the city you live in now or where you intend to build your career.

Obviously, once you invest the time to become successfully established as a mid-level data scientist or even take on a managerial role, you can expect the salaries to increase exponentially. At a top company, this might come pretty close to the upper end of BLS salary projections. Not only that, but many companies will also offer large annual bonuses and even shares or stocks as additional perks, particularly for a role as impacting as a data scientist and the connection that work product has to the overall performance and success of the company as a whole.

Speaking of perks, data science is also one of the most highly lucrative careers that generally allows you to work fully remotely. Working from home is a benefit that has always been beneficial, but which also grew from a luxury to a necessity in the eyes of many when the 2020-2021 COVID-19 pandemic caused the modern workforce to rethink how we conduct our professional lives. This same benefit is often not available to other careers with equally high average base salaries such as medical professionals, attorneys, and engineers.

It may seem mercenary to point out making a lot of money as a top reason to pursue a given career, and it should go without saying that no amount of money makes a job worthwhile if you simply do not enjoy what you do. Though it’s understandable that the job security and benefits are in large part an influencing if not deciding factor in how, when, and where to launch a chosen career path. In that area of consideration and management, data wins. (Almost ironically, the data exists to prove it.) If you have a passion for data science and the skills and knowledge base to succeed at it, you’re set for a highly lucrative career that provides comfortable, reliable income and allows you to build a secure foundation for your life and family.

Providing further supporting evidence of this comfort and security, just as the BLS tells us that data scientist can be an extremely high-paying career, a Glassdoor editorial claims it’s one of the best for work-life balance. That combination is pretty tough to beat.

4. Data, an Ever-Evolving Field

According to The Economist, data has supplanted oil as the most valuable resource in the world. This could easily rank as the number one reason to pursue a career or education in data science. The reason it’s at number four on this list is the relative unfamiliarity of this fact to future or emerging students and experts, relative to what we described above regarding data science’s slow trickle into the public school curriculum as a stable subject and career industry option (i.e., required “economics” classes that establish a general understanding and ignite professional passions and interests of future economists). Data science’s presence in traditional education does not yet match projections for its role in or contribution to our society including the quantity of professions for graduates.

Estimates say that as many as 97% of all businesses utilize regularly as part of their strategic decision-making. By harnessing data, companies are gaining an unprecedented level of new, valuable insights into their existing and potential customers than ever before and so the field of data science is evolving every day.

The ever-evolving nature of data science is part of what makes it such a compelling industry to build your career. You’ll gain a wide array of new skills that will allow you to leverage data to aid companies with their business strategies, and explore exciting new fields developing from within data science—fields like artificial intelligence, machine learning, big data, and more.

You’re probably already familiar with artificial intelligence (AI), at least as a concept. Designed to simulate human intelligence in machines, AI uses multiple algorithms to perform autonomous actions and to understand relationships between different types and different pieces of data. Machine learning is an offshoot discipline of AI focusing on developing machines that will learn from past data automatically without explicit programming.

AI and machine learning are tools used to aid in more efficiently and accurately analyzing the data used in data science. As data science continues to evolve, the data that’s available to us becomes more and more complex and it can be a struggle to decipher all on our own. Machine learning lets machines handle a lot of that heavy lifting for us and is used in things we use every day like Google’s search engine and Spotify’s song-choosing algorithms. Though still a developing technology, machine learning is starting to play a role in advances as diverse and influential as self-driving cars and cancer detection. A seemingly elusive concept to some in name (machine learning), it’s actually a discipline that effects individuals on a very practical, daily, and human level.

As data science grows, so will the opportunities it presents. Ten years ago, it would have been difficult to imagine where the fields of data science, AI, and machine learning would take us. It’s equally tough to predict where we’ll be ten years from now, though almost inarguable that it will be a path of increase and growth. If you become a data scientist, those answers will be, in part, up to you and your career contribution influencing in what direction you take the industry in next.

5. Diverse Career Growth Options for Data Scientists

We think we’ve demonstrated why a career as a data scientist is an incredibly rewarding one, but eventually the day may come where you’re looking to branch out into a new role. With a background in data science, you’ll be well positioned to succeed in one of the widest variety of continuation careers of any industry.

If you’re looking to stay strictly in the data science role, obviously you can always work up to advanced leadership roles in management and senior or lead data scientists which can net you tens of thousands more in salary while giving you more responsibility and the opportunity to chart the course of the work your company is doing. Alternately, you may choose to specialize, in something like data engineering or machine learning engineering and pursue career growth in a concentrated path.

There are other directions to go, however. As we’ve discussed, data science isn’t just an IT role, it’s a business role too. Data scientists regularly interact with the VIPs at their company, working with C-suite executives. They’re working side-by-side with the true movers and shakers of the company, solving business problems and building a solid network of connections while they’re at it. As such, many data scientists go on to executive roles themselves in roles like a chief data officer.

Such roles require a wealth of management skills, however, and may not involve a lot of day-to-day work with data, so you’ll want to pursue data degree programs that match your projected career goals within the industry. Many of today’s leading schools offering degrees in data science have designed their academic programs and curriculum around knowledge of this industry dynamic, resulting in business based data degrees or vice versa.

You can also use your business experience, communication skills, and professional network to strike out on your own and start your own business or brand as a data science consultant. This will allow you to choose your own clients, your own industry focus, your own methodology and, of course, your own hours. More specifically, coming full circle with our number on reason listed to become a data scientist, you’ll be able to personally tailor and customize your data-driven impact in your community or in global trends, through your individual vision and contribution to the evolving methodology and worth of data.

Ultimately, the choice is yours, because when you put in five to six years in the data science industry and have something to show for it, you may well be able to write your own ticket to wherever you’d like to go within the industry.

2021 US Bureau of Labor Statistics salary and employment figures for data scientists reflect national data, not school-specific information. Conditions in your area may vary. Data accessed January 2023.