5 Steps To Build a Career in Data Science and Land Top Data Science Jobs in 2024.

Last updated on April 25th, 2024

November 11th 2022

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  • A career in Data Science is one of the most popular choices in the job market among young professionals today. Not without a reason — Data Science jobs are well-paid, offer great personal growth opportunities and can be carried out in a remote setting.

  • In this article, we list tips for how to become a competitive Data Scientist, from choosing the sub discipline of Data Science to composing a project portfolio and applying for jobs.

Starting a Career in Data Science in 2024: Is Are Data Scientist Careers Still Hot?

A career in data science is one of the most popular choices in the job market among young professionals today. It’s one of the most popular career paths 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 is a grawing interdisciplinary field that consists of machine learning, statistics, computer science and domain expertise. For this reason, in general, data science jobs today are well-paid, offer great flexibility in terms of the scope of possible career choices or career options 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 2024? 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.

Especially ever since chatbots have come out, the art of Data Science has become trickier than ever. While just a few years back, one could easily find a job in “Data Science” and run linear regression for the whole day, today, simple jobs in Data Science are brutally automatized and delegated to machines.

Therefore, today, any fresh Data Scientist should aspire to learn how to plan and execute complex projects, preferably combining background domain knowledge with skills in statistics and programming.

As a rule of thumb, working on the backend is safer than working on the frontend, and working on operations (on projects that contain multiple components and involve communication with multiple stakeholders) is safer than focused work on one aspect of the projects.

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.

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However, it seems that the market might have saturated indeed. How to make the right career decision and choose the best career option 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 knows what the job market will look like ten years from now.

Therefore, your best bet is to choose your talents and interests and make sure that you optimally develop them. If you believe that you have a passion for data analysis and building new solutions using your analytical thinking and code, go for it and work on your professional development!

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The 5 Steps To Become a Competitive Data Scientist.

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 Data scientists 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 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 Data Scientist relates to those who apply established methods but in new applications, or 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 your contacts with the Data Science world!

(c) Data Scientist of Type 3: None 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 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 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.

Match Your Programming Skills With a Specific Background.

This part is relevant especially since chatbots such as ChatGPT have come to life. Today, we observe a massive shift in the job market from Data Science to 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 to build your optimal career path.

Problem-solvers Are Preferred in the World of 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 the 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 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.

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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 on the career path 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, and 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 it in practice. In Data Science, no one will hire you based on 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.

Start Simple and Develop Deep Understanding For Algorithms.

Unlike ten years ago, today, the level of automatization is high. Back then, you had to program a deep neural network from scratch. Today, in most instances, you no longer implement cost functions by yourself. Most popular algorithms slowly become black boxes. You just need to feed in a few parameters into a Python package for building neural nets and off you go! 

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 pre-coded packages, and play with all free parameters as long as it takes to get an intuitive understanding how the algorithm works. This effort will certainly pay off when it comes to debugging code and looking for design issues.

How to start then? At first, the world of Data Science looks enormous and hard to comprehend. If you will jump on the whole subject and expect to learn complex concepts such as, in example, Generative Adversarial Networks, on day 1, you will be disappointed.

The good news is, in fact, machine learning becomes much simpler if you start from simple, basic concepts as complex machine learning algorithms are usually just combinations or variations of the simpler ones. One classic textbook on machine learning that we can recommend is “Pattern Recognition and Machine Learning” by Christopher Bishop.

Become a Polyglot.

Most teams and 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 logic just once, you will learn the second language much faster. 

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3. Build a Professional 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.

The access to forums where you can meet other students at the same career stage as you, who are now in the process of learning the same content as you, is essential to keep high level of motivation and making progress as a student and building a professional network. 

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

Even if you don’t win such a competition, you still get trained in executing projects with clear deadlines. Plus, you meet new people in the process. And if you win, it’s a perfect selling point at the onset of your Data Science career. 

(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, for example, 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 career advancement managers. To exist as a professional, you need to be easy to find. Of course, it doesn’t require becoming an influencer! You just need to become visible enough so that people looking for talent can find you. And they WILL find you as Data Science is still in high demand today.

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 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 regularly, you will see the results. If you write one post per month on your 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!

In terms of advancing your professional development, the time spent on LinkedIn will give you the highest returns. To learn more on how to create a compelling LinkedIn profile and effectively network on LinkedIn, please check our articles “10 Steps to Create an Effective LinkedIn Profile” and “Top 11 Rules For Effective Networking on LinkedIn.”

(e) Join online communities for developers and Data Scientists.

There are hundreds of online groups where you can freely share information and learn from experts and other students of Data Science. 

Reddit and Discord are known as the to-go-to platforms for developers. You can join, for example, the Data Science community on Reddit (almost a million members!) or the Data Science Discord server. 

You could also join LinkedIn groups such as, for instance, Machine Learning Community or Data Science Central where you might find not only knowledge but also Data Science job postings and open projects.

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4. Learn How To Code In a Team.

Today, the vast majority of Data Science projects is performed in teams rather than individually. If you don’t know any framework for collaborative coding, you will have hard time finding your find Data Science job.

So, what are the necessary competencies in terms of collaborative software development? Roughly, they can be classified into three categories:

(a) Tools for collaborative coding.

The most classic tools for collaborative coding include version control tools such as Github and Gitlab.

However, to become a full-fledged team player in Data Science, you should also learn something about semantic versioning and changelog, and master using online notebooks for interactive coding in a team: Jupiter Notebook and Google Collab.

(b) Online environments for project deployment.

Cloud computing is a norm in today’s Data Science. Most employers will expect you to be familiar with at least one environment for project deployment, such as Amazon AWS, Microsoft Azure, Google Cloud, or Heroku – or learn soon after you start your job.

(c) Writing technical documentation.

For most homegrown developers, writing documentation is the least favorite part of the job. However, for employers, the ability to make your code understandable to other team members is a crucial competence.

In the employer’s eyes, less efficient but more understandable and better documented piece of code can be more valuable! Needless to say, mentioning at the job interview that you always properly document your code can get you a job.

How to write proper technical documentation for your code? It’s a matter fo practice, but you can find tips for beginners here.

5. Collect Projects Not Positions.

Lastly, remember that as a future Data Scientist, you need to create your professional portfolio by projects, not positions. Many Data Scientists today (especially in the IT industry) change jobs like gloves, only because they can. They fish for any salary raise and any diagonal promotion they can possibly get, without thinking about strategy when choosing projects.

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 on your career path.

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.

Furthermore, while learning Data Science, remember to load your projects onto your Github profile and properly document them. In fact, while applying for jobs as a Data Scientist or Machine Learning engineer, the technical recruiter will treat your public project portfolio as your CV – your diplomas and certificates won’t interest them as much.

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To Study or Not To Study?

Lastly, to study or not to study?

In a recent interview Merck CEO, Kenneth Frazier and former IBM CEO, Virginia “Ginni” Rometty both admitted that university degrees are not as indicative of professional excellence as it used to be in the past.

Indeed, today, university diploma is mostly a way to make the early-stage recruiters’ jobs easier. They often have no specialistic knowledge on the offered positions and an academic title is nothing else than a heuristics, or a stamp of quality that gives them evidence that the candidate has some knowledge in the field instead of just saying so. And that’s all it is.

This means that you can technically start working as a Data Scientist without any formal education, even teams in the leaders of innovation such as IBM or Merck. However, the decision to enroll for a Master program in Data Science in much more complex and does not only pivot around the diploma itself. 

Undergraduate studies are not only taken to receive a diploma. They also serve to gain experience together with other young people, make lifelong friends, learn to learn, and get disciplined for life by adhering to deadlines.

Also, remember that there are different levels of understanding in Data Science. Some innovative companies such as Google prefer to accept candidates with high academic education for a reason: it not only gives you skills, but also ensures profound, systematic knowledge about algorithm complexity, efficiency, boundary conditions, et cetera.

To find relevant Master Programs in Data Science, you can, for instance, search through the open “Masters in Data Science” database.

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Conclusions: Building a Career in Data Science in 2024 Still Looks Shiny! Data Science Jobs Are Waiting.

So sum up, a competitive Data Scientist not only fluent in programming and statistics, and educated in the domain of machine learning models, but also possess specialist domain knowledge. A LinkedIn user Praveen perfectly grasped this concept in his infographic:

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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.

This might be good news if you come from another discipline! Specialize as early as possible, work on your professional development 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.

Please find some more predictions on the future of the job market in times of AI in Natalia Bielczyk’s lecture “Are we Doomed? AI and Your Job.”

Best of luck with developing your Data Science career in 2024! 🙂

Other Resources That Will Help You Start a Career in Data Science.

Lastly, we highly recommend some other resources that will help you start in Data Science. Data Science is not a secret craft that is only available to the few. You don’t need to attend commercial courses to learn all you need from scratch!

1. Our Materials.

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.

Please also find our other articles on new trends in the IT industry and Data Science in particular:

2. The “YouTube University.”

As a matter of fact, today, one can learn almost any professional skill directly and for free from YouTube – hence, “the YouTube University.” For instance, you can successfully start your Data Science career by going through open-source tutorials such as “Learn Data Science Tutorial: Full Course for Beginners” from the freeCodeCamp YouTube channel.

3. Professional Certificates from Top Tech Brands.

Furthermore, many top tech companies release free professional courses at platforms such as Coursera. Taking such a course will allow you build a solid base of skills in data science, machine learning, and Python programming. 

Additionally, your certificate obtained from the course might be recognized by the company during recruitment and as such, open doors for you.

Just to mention the two most popular Data Science certifications:

1. IBM Data Science Professional Certificate.

By taking this course, you can get started in the in-demand field of data science with a Professional Certificate from IBM.

During the course, you will learn the foundations of data science and develop hands-on skills using the tools, languages, and libraries used by professional data scientists. You will also learn Python & SQL, analyze & visualize data, build machine learning models.

2. Google Data Analytics Professional Certificate.

In this program, you will acquire in-demand skills that will have you job-ready in less than 6 months. You will learn how to process and analyze data, use key analysis tools, apply R programming, and create visualizations that can inform key business decisions.

4. New AI Tools.

Today, AI-based tools useful for programming professionals  including Data Scientists come to market every single day. Visit webpage thereisanaiforthat.com and experiment with novel tools to stay in touch with the recent developments. 

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Please cite as:

Bielczyk, N. (2024, January 29th). 5 Steps To Build a Career in Data Science and Land Top Data Science Jobs in 2024. Retrieved from https://ontologyofvalue.com/career-in-data-science-five-steps-to-become-a-data-scientist-and-land-data-science-jobs/

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