July 12, 2021 | 5 Steps To Build a Career in Data Science and Land Top Data Science Jobs in 2021-2022

career in data science 5 steps to success

Starting a Career in Data Science in 2021: Is a Job of a Data Scientist Still Hot?

A career in data science is one of the most popular choices among young professionals today. It’s one of the most popular careers after PhD, as well as the door to industry for enthusiasts of programming as well as graduates in computer science, mathematics, and multiple STEM sciences. Data science jobs today are well-paid, offer great flexibility in terms of the scope of possible career choices, and can be carried out in a remote setting. Which is particularly attractive to the young generation of digital nomads.

However, as a student or a professional pondering career options at the moment, you might be asking yourself the question: is it still worth joining this field now in 2021? Or, is data science in a bubble? Is starting a career in data science still a good choice, or, am I about to become redundant in just a few years from now? 

To put it briefly: yes, developing a great career in data science is still possible. Yet, the rules of the game have changed as compared to 5-10 years ago.

Timing The Market – Not The Best Idea!

In recent years, the number of data science jobs blew up. According to Google Trends, starting about 2014, the terms “data science” and “machine learning” have been increasingly popular.

data science machine learning google trends

However, it seems that the market might have saturated indeed. How to make the right career decision in such a situation?

Well, one thing to remember is that you cannot time the market. You cannot say, “There are too many Data Scientists today, there is just no more room for me!” or “This market is oversaturated, it won’t pay off for me to join right now.” No one really knows what the job market will look like ten years from now. Therefore, your best bet is to choose for your talents and interests and make sure that you optimally develop them. Therefore, if you believe that you have a passion for data analysis and building new solutions using your analytical thinking and code, go for it!

This article lists five steps that will maximize your chances to become a pro in Data Science- all the way from your first line of code to becoming a reputable Senior Data Scientist.

1. Learn Your Edge Before You Run For a Position Of a Data Scientist

This is the first decision you need to take, yet, it will define your whole career in data science. There are three types of a Data Scientist today.

(a) Data Scientist of type 1: Designer of New Methods

Of course, data Science is an open-ended problem, and new methods for data analysis are created every day. If you have a strong analytical mind, a strong background in mathematics and/or programming, and a sweet tooth for designing new analytical solutions, you might choose to become a creator of new algorithms. The work of a “Data Scientist of Type 1” often goes beyond algorithms and ends up with proving new theorems or designing new estimates. It might even lead to a peer-reviewed research publication!

Especially large IT firms seek such people, as new protocols and algorithms are the fundaments of large-scale innovation. Of course, this is a challenging path and only the best Data Scientists can follow this one. However, if you feel like you are the one to do it, you should clearly communicate it to your potential future employers, as it will boost your value in their eyes!

(b) Data Scientist of Type 2: Designer of New Products and Services

The second type of a Data Scientist relates to those who apply established methods but in new applications, or in new combinations. For these applied Data Scientists, the novelty lies in using the set of available methods to solve a new problem or to find a new solution to an existing problem. The work of a “Data Scientist of Type 2” often goes beyond algorithms and has much to do with Business Intelligence.

No need to say that these Data Scientists of type 2 are also highly desired in the job market today. Therefore, if you believe you can become one, pitch for this in your resume and in your personal contacts with the Data Science world!

(c) Data Scientist of Type 3: Neither of the above

These are the Data Scientists who don’t fall into any of the aforementioned types. They know Data Science, and they tweak some parameters in some algorithms to improve on the existing pipelines. However, they don’t create much novelty, either from the methods’ or applications’ perspective. Rule no 1: don’t ever become a Data Scientist of type 3! This path might be a trap and end up with becoming redundant in the perspective of 5-10 years. Instead, learn your edge, and decide whether you prefer to become a Data Scientist of Type 1, or Type 2.

2. Learn at least one POPULAR, OPEN-SOURCE Programming Language, and Practice, Practice, Practice

It’s no secret that the more popular and applied a given language is, the more career options you will eventually get. Some programmers go with quite the opposite strategy and consciously choose to learn some niche language (such as F#, Scala, R, J, or Clojure) hoping to become unique and indispensable this way. However, more often than not, becoming a niche programmer limits your opportunities in the long term.

You should learn an extra niche language only after you know at least one of the top, lingua-franca languages–which are, currently, Python, Java, C. In today’s data science, especially Python is a supreme tool that will allow you to work almost anywhere and with anyone. However, the scope of tools necessary to conduct projects always depends on the discipline. Therefore, you should do your research first, before taking to an ultimate decision on which programming language to learn.

And no wonder that Python has outlasted Matlab in the range of data science applications, as it is an open-source language. As a rule of thumb, open-source environments will always outcompete commercial platforms as thanks to their enthusiasts, they will always develop faster.

Practice Makes Perfect

Furthermore, once you decide which programming language you are willing to study, make sure that you learn in practice. In Data Science, no one will hire you on the basis of your completed courses or recommendation letters. The only thing that will interest your future employer, will be your completed projects. Therefore, choose courses that allow you to take little projects onto your plate, and post the results on GitHub. Whatever new method in Data Science you master, it should lead to some updates in your GitHub profile! Remember that in data science, your GitHub account is your resume.

Go Deep

Unlike ten years ago, today, the level of automatization is high. In most instances, you no longer implement cost functions by yourself. Algorithms slowly become black boxes. Many data scientists no longer can program the analysis from scratch but rather, they go the lazy way. They just load Python packages and learn how to tweak some of the parameters depending on the problem. They like to “understand algorithms conceptually” rather than understanding every step of the pipeline in detail. 

Therefore, while learning, make sure that you get to the bottom of things. If you learn about a particular class of algorithms, code a simple example by hand rather than just installing and running precoded packages. This effort will certainly pay off when it comes to debugging code and looking for design issues.

Become a Polyglot

Most teams and most companies today operate using a combination of multiple programming languages. Some languages work well in a package. For instance, if you want to build websites using JavaScript, you will likely need to learn PHP and MySQL to handle the backend properly. On the good side of things, once you learn the logic just once, you will learn the second language much faster. 

3. Build a Network As a Data Scientist

Today, networking is more important in career development than ever—opportunities come with the territory. Therefore, while learning skills necessary to work as a Data Scientist, make sure that:

(a) You only join online courses and education platforms that are associated with online communities.

Having access to other students who are now in the process of learning the same content as you, is essential to make progress, and build a professional network. 

(b) You take part in online competitions such as Kaggle contests. 

Even if you don’t win, you still get trained in executing projects with clear deadlines. Plus, you meet new people in the process.

(c) You attend meetups and events, including hackathons and small local conferences.

Face-to-face meetups and working hand-in-hand are the best ways to build bonds for years to come and meet potential future collaborators or employers. Furthermore, by taking part in short group projects, e.g., at hackathons, you learn and practice the workflows popular in Data Science such as scrum.

(d) You build an online presence.

Today we all need to be our own managers. Make sure that you have enough visibility online: a landing page where your bio, resume, and photo can be found, an updated LinkedIn profile, and some written content that indicates your expertise in the field. It can be a blog on your personal website, a Medium profile, or a series of posts/articles on LinkedIn. 

Building a name is a process that lasts for years. However, if you make updates on a regular basis, you will see the results. If you write one post per month on your personal website, keep the content consistent in scope, and optimize it well for SEO, then after 2-3 years you will get quite substantial organic traffic in the range of 10k visits per month or more. That opens tons of new opportunities!

4. Match Your Programming Skills With a Specific Background

Today, we observe a massive shift in the job market from Data Science towards Machine Learning Engineering. The terms are similar, and the difference between them is not clearly defined. However, as a rule of thumb, machine learning engineering is more related to working on a particular class of problems related to a particular type of data, e.g., nanomaterials, cryptography, or dermatology. On the contrary, Data Science is focused on using algorithms in a wide variety of applications.

Most likely, in the future, Data Science jobs will be easier to automatize than machine learning engineering jobs. It is because specialistic problems require combining programming skills with many years of expertise in a specific discipline. Therefore, hone your background knowledge and whatever that is, make sure that you use it in your Data Science career! Find one branch of Data Science where you can shine given your prior knowledge, combine this knowledge with your newly acquired programming skills, and apply for jobs accordingly.

Problem-solvers Are Preferred For Data Science Jobs!

Remember also that every maturating market shifts toward specialization. Since 2014, we have been observing a massive growth in data science. In this early wave, every pair of hands was wanted in this space. Now, this is not enough to be valued – to get far, you need to also present expertise and some level of success in solving specific types of problems.

As also discussed in the article “The Jobs of the Future,” it is likely that in the job market of tomorrow, we will refer to our jobs not by position titles but by types of problems we are focused on. Therefore, it is beneficial to start thinking about your craft right now. As a professional, you should sell yourself as a package. Namely, as a person with great technical skills who has a mindset for solving problems and a personal mission.

And especially today, after the world pandemic, you will no longer be able to compete on the price. In IT and data science, the default working mode is the remote work today. It is so easy for businesses to outsource Data Science jobs these days! Therefore, if you wish to become a successful Data Scientist, you need to compete on the skill, not on the price.

5. To Land Data Science Jobs, Collect Projects Not Positions

Lastly, remember that as a Data Scientist, you create your professional portfolio by virtue of projects, not positions. Many Data Scientists today (especially in the IT industry) change jobs like gloves, only because they can. They fish for the raise and take any chance to get a higher paycheck. Although it is understandable at a human level, it is also a short-lived strategy. At the end of the day, the success of your company becomes your success. The workflow in data science is project-based. Therefore, if you drop out before wrapping up your open projects, the hiring manager might take it as a sign of low integrity.

In general, successful managers in IT paved their way with projects not positions. For instance, the former Yahoo!’s CEO, Marissa Mayer, got her seat not because she had served as the head of Consumer Web Products, or Vice President of Search Products and User Experience at Google in the past. She got the job because she was involved in the development and launch of Google AdWords, Gmail, Google Search, Google Images, Google News, Google Maps, Google Books, Google Toolbar and iGoogle.

Therefore, it is better to wait until the end of the current project or until the product launch before you decide to change your position. When you start listing your prior positions at the job interview, the first question will be, “What projects were you involved in?” If you were never involved in projects until the very end, it will mean to the hiring manager that you achieved no success at all.

career in data science

Conclusions: Building a Career in Data Science in 2021-2022 Still Looks Shiny! Data Science Jobs Are Waiting

The market for Data Science jobs is still growing and the growth doesn’t seem to end any time soon. However, as mentioned above, the data science market is maturating. Today, you need to make sure that you present more than just bare programming skills. Which might be a good news if you come from another discipline! Specialize as early as possible, and integrate your background into your expertise in data science. And remember, compete on the skill, not on the price.

Mind also that, given the popularity of data science as a professional field and the constant inflow of new people, the entry-level salaries tend to go down as compared to 3-5 years ago. Therefore, if you start in this discipline right now, you might need to take a hit in your earnings for the first year or two. You might even need to start from an internship instead of an employment contract.

On the good side of things, today, there is high demand for senior experts in data science. If you stay in the field for 2+ years and build up to a certain level of expertise, you will become a valued employee with a lot of options for your further career development.

Good luck with developing your data Science career in 2021! 🙂

More Resources on Starting a Career in Data Science

If you would like to hear more advice for beginners in Data Science who enter the job market today, please watch the episodes of our Career Talks starring Fabio Gori, PhD, and Caolan Kovach-Orr, PhD, who talk about the process of hiring Data Scientists both from the perspective of a Data Scientist and a Hiring Manager.

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Please cite as:

Bielczyk, N. (2021, July 12th). 5 Steps To Build a Career in Data Science and Land Top Data Science Jobs in 2021-2022. Retrieved from http://ontologyofvalue.com/all-posts/career-in-data-science-five-steps-to-become-a-data-scientist-and-land-data-science-jobs

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Where Can You Develop a Career In Data Science In the Job Market? 

Lastly, Data Scientists can work anywhere today. From corporations, through consultancy companies and startups, to launching your own freelancing business – there are so many options! Are you not sure which working environment you should choose to have a good start in your career? Check our new self-navigation tool, The Ontology of Value Test!  This test will show you where you fit in the job market given your natural working style, personality, and values. The test was built using, nomen omen, Data Science, and it will give you a great overview of the potential and the opportunities that you have in the job market of today! https://ontologyofvaluetest.com 

Acknowledgments

Cordial thanks to Piotr Migdał, PhD, Alican Noyan, PhD, Caolan Kovach-Orr, PhD, Fabio Gori, PhD, Aleksandra Drozd, PhD who contributed to this material sharing their experience working as Data Scientist through this blog or and/our YouTube channel

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