As we inch further into the year, I have seen more and more postings for data science positions, especially on LinkedIn, and other similar job-posting sites. After an expected lull due to current events, companies have figured out their budget and focus. Some of those companies include newer data science positions that they need to hire as soon as possible or in the near future.

There are several reasons for becoming a data scientist. I am going to highlight five main reasons I became a data scientist, and hopefully, it can align with some of the reasons why you would become one as well.

As with many positions that have any general set of expected skills, data science is no exception, and can usually be thought to have these skills that I will outline below. Of course, there are others, but I will focus on the skills I come across the most at various companies as a data scientist.

— the heavily debated Python versus R is usually controversial, but ultimately, it just depends on what the company is already using as their main programming language. Sometimes, data scientists can work alone and form models and output results directly to a stakeholder, and usually refer more to R in this case. However, in my experience, it has been easier to work cross-functionally with both data engineers and software engineers with the use of Python. This language is oftentimes used for deployment purposes, so, it can be easier to start with Python from the start. The benefit is that in the process of learning data science, you will learn Python or R, which will help you earn a variety of skills that can support you better down the road if you chose a different career path such as software development.

— another popular skill for data scientists is SQL. Sometimes, online courses and universities neglect to stress the importance of how widely used this language is for data scientists. It is nearly used for every project I work on because the dataset is not simply given to you. You have to make your own dataset, and that involves querying your database tables with SQL. Like Python (and somewhat R), learning SQL is useful not only for data science but for data engineering and data analytics as well.

— while this skill is not a programming language, it is still important. Business, more so a concept, is something every data scientist learns. Similarly to SQL, it is not taught in education settings nearly as much as it should. What I mean by the business is that you need to really get used to jumping into situations that are not strictly just data science. The business uses data scientists to either make a process more efficient or find insights that will change the business in the future. Oftentimes, education for data science will focus so much on obtaining the highest accuracy for say, segmenting different types of customers. It can be great to achieve 98% accuracy, but if you are not able to come up with a plan for how you would implement the model and its results thereafter, then your model is useless.

You need to know that stakeholders, CEO’s, C-Suite/higher leadership, will ask what you will do with your results to change the business. So in turn, you would want to apply those customer segmentation groups to a marketing campaign through various, targeted emails. Then, you would create a test of some sorts to see how the emails performed, say with an AB test. As you can see, just having an extremely accurate model is just one part of the data science and business process. Practicing this business process over and over again is extremely beneficial.

— there was more focus on statistics in school, and it can prove to solve many problems for a data scientist. Knowing statistics is critical for data scientists, as it is the foundation of machine learning models. Practicing analysis of variance, or population sampling, etc, is useful in several forms of the business, say marketing campaigns again, or AB testing.

The growing field of data science may, at first, seem that the position is not as unique as it used to be. However, it is still just as unique, and even more unique at the specific company you will be working at. There may be other roles like security engineers that could possibly be more unique, but data science is one-of-a-kind.

To expound on small headcount, software engineers, where even a small tech company can be comprised of near 30 developers, will usually have anywhere from one to four data scientists. When your role is this unique, you can learn valuable skills, touch multiple departments, and impact your company significantly. That is not to say the other aforementioned fields lack these benefits, but I do believe you are more likely to encounter various parts of the business in data science. Ultimately, you will feel great about your everyday work. This benefit leads me to my next point — impact.

The impact a data scientist can make is outstanding. You can automate previously manual processes, saving the company thousands or even millions of dollars. You can save your company time, and allocate time better spent. The projects you will work on are various in nature and importance.

For example, I worked on a project that automated a large portion of a manual process, with high accuracy. It was truly amazing to feel how impactful you can be on the business. The best feeling, however, is the impact you can make on society, health, etc. There are countless ways to have a positive impact on something with data science, and your day-to-day work is no exception.

Before the current state of The World, remote work was already a prevalent benefit of tech roles, especially that are in data science. Unfortunately, there are several types of careers that cannot benefit from this point, which I admire extremely and am thankful for.

If you like to work from home, then data science will be an excellent opportunity for you. There are severe tools and platforms that aid in creating a successful environment without a physical office. You can use video conferencing, messaging, and project management as well as versioning tools. Tools include, but are not limited to:

* Zoom
* Slack
* GitHub
* Jira
* Confluence

Working from home is personally a huge benefit for me. I see it as an opportunity to enjoy my day more. Living in a city that can give you hours of traffic can be not the best feeling, so being able to eliminate that completely is an enormous positive.

Yes, data science pays well. I wanted to make sure I included this as the last benefit, as not only is it well known already, but it is not the most important factor in deciding on a career. While more money is great, if you do not like your field, then you will be miserable. However, if you enjoy data science, and expect to build your brand and career in it, then you can expect to have high payouts.

Of course, there are variants between states and even cities in those states, so you can expect different ranges depending on where you live. Some companies offer large bonuses annually as well. Because your role is incredibly impactful, you can also expect shares or stocks in a company at some companies.

Additionally, depending on the job description or job functionality, you can expect variations in salary. Points to consider when negotiating for a data scientist salary include, but are not limited to:

Photo by Patrick Perkins on Unsplash [3].

As you can see, there are several reasons for becoming a data scientist, especially in 2020. The top five reasons to become a data scientist are: the variety of skills you will learn along the way, uniqueness in your company, impact on your company, remote — work from home, and pay. Data science may not go away for a while and could very well become even more of a popular career. It is important to keep in mind that there are branches of data science like business intelligence, software engineering, and machine learning that are also great careers. Hopefully, you will become a data scientist, and will at least experience these five beneficial reasons for yourself.

[1] Photo by Csaba Balazs on Unsplash, (2018)

[2] Glassdoor, Data Scientist Salary, (2008–2020)

[3] Photo by Patrick Perkins on Unsplash, (2020)