Top Data Science career paths and jobs in 2020

October 27, 2020

The demand for skilled Data Science professionals outweighs the supply. This is an up-and-coming field, and organizations are willing to invest more and more to fill their open job positions.

There have been almost 13,000 Data Science job postings in 2019-2020 on LinkedIn and Glassdoor across the U.S. and Europe. Glassdoor named Data Scientist the best job in the United States in 2019, while it ranks number 1 LinkedIn’s top 10 jobs.

As for Europe, a report by the European Commission states that the number of data workers in Europe will grow to over 10 million by the end of 2020. A forecast by the EU predicts that there will be almost 800,000 unfilled data positions by the end of 2020. Based on this projection, 100,000 new data-related jobs will be created by the end of this year.

Clearly, Data Science is a field that is going places fast. So, let's take a closer look at the career paths that are out there for Data Scientists.

The Top 4 Data Science Career Options in 2020

You could compare the current trends in Data Science to those of Computer Science a decade ago. In other words, this is a discipline that overlaps with almost every progressive career path there is. Virtually any interaction with technology includes data, whether it’s making a purchase on Amazon, checking your Facebook feed, finding something to watch on Netflix, or even signing into your phone using facial recognition.

2.5 quintillion bytes of data are produced by humans every day. And someone needs to take care of it all.

So, who are these brave souls working tirelessly to get all this data in order? You might hear them referred to as Data Analysts, Data Scientists, Data Engineers, Machine Learning Engineers, or one of many other titles. While there are some specific features to each of these roles, there is also no strict definition as to what they entail as different companies and sectors have different expectations and needs with regards to data specialists.

Let’s briefly take a look at what is typically covered in each of these roles.

Data Analysts

A Data Analyst's primary goal is to get useful business insights from data while working with massive amounts of it. Usually, Data Analysts are given specific questions that need to be answered in order to make business decisions. Data cleaning and sorting, adapting calculations, and visualization are all big parts of a Data Analyst’s everyday work.

Data Scientists

Data Scientists work with data in a similar way to Data Analysts, but they also build machine learning models to make predictions and forecasts based on past data. This role is the most general, and the tasks depend on the company. So, while a Data Analyst provides answers to questions, the role of a Data Scientist is more proactive - they look for trends and provide new insights and suggestions without being asked. Data storytelling is one of the key skills that Data Scientists need to have in order to deliver their research results in a compelling and understandable way.

Data Engineers

These specialists are in charge of a company's big data ecosystem. Data Engineers create the infrastructure for data, so that Data Scientists can then analyze it. The data is usually stored in a data warehouse. To put it simply, these engineers are responsible for ensuring that Data Scientists have all the needed data from various sources. If the Data Scientists are missing something, it’s the Data Engineer’s job to make sure they get it. Advanced programming skills are key when working as a Data Engineer.

Machine Learning Engineers

A Machine Learning Engineer’s work consists of developing machine learning models. While Data Engineers are responsible for the whole data ecosystem, Machine Learning Engineers create machine learning models that make predictions, which are later integrated into the product. A Machine Learning Engineer’s role is somewhat similar to that of a Data Scientist - both roles build machine learning models. However, the latter role requires strong data storytelling skills, while Machine Learning Engineers focus more on creating the most accurate machine learning models.

Exciting data roles in a wide range of industries

Data experts are needed in virtually every sector, not just in tech. Health, finance, trade, telecommunications, manufacturing, retail, utilities, and even agriculture are all sectors where data is essential.

That’s because in all of these areas, making mission-critical business decisions is impossible without a data-driven approach. This means that as a data professional, you will be playing a critical role in the success of your company or organisation. Without you, all of their raw data remains just potential. With you, this potential is transformed into actionable insights and real innovation.

And the most exciting part is that the tech sector is not the only option for data scientists or data engineers - there are plenty of industries to choose from. This means you can make an impact in a field you are truly passionate about, be it finance, travel, or healthcare.

Global data professional salary ranges in 2020

The ability to become a key part of the decision making process in a company you care about is clearly one great reason to become a data professional.

But what about the salary levels and financial rewards??

Well, the average salary for a Data Scientist is around $100,000 per year, according to the U.S. Bureau of Labor Statistics.

A Burtch-Works study of Data Science salaries in 2020 reported the following salary levels:

  • Salaries for entry-level data scientists remain high, at $95,000 per year.
  • A mid-level data scientist can expect a salary of $130,000. If this data scientist is also in a C-level role, the average salary rises to $195,000.
  • Salaries for experienced Data Scientists average $165,000, while the salary for an experienced management-level professional is considerably higher, at $250,000.

Salary ranges of data professionals in Lithuania in 2020

As for Lithuania, major international companies such as Tesonet, Vinted, TransferGo, Genus AI, Revolut, Swedbank, SEB, and many more are all actively looking for data specialists on Linkedin. As a result, gross monthly salaries for data scientists range from €2,000 to €8,000. This is well above average salary levels in Lithuania for other professions, making Data Science a well-paid area of work.

Research on Data Science roles in Lithuania conducted by MeetFrank shows that companies are finding it challenging in 2020 to find specialists to work in the data and analytics field. In short, demand is much higher than supply.

A smart career decision for today and tomorrow

The trend is clear. The amount of data available is continuously increasing. What’s more, executives are increasingly dependent on this data for making well informed decisions. So the demand for Data Scientists is going to grow and grow. Companies will continue to compete for exceptional Data Scientists who can enable them to make data-driven business decisions.

Data Science is a field that is both well paid and rewarding, and the career options in this field are widening quickly. If you have a passion for computers, math, and discovering answers through data analysis, you should consider studying to become a Data Scientist.

Enroll in our Data Science curriculum

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