The Top Data Science Jobs of the Future

Data science is the key to our future. For that reason, those interested in working in the field are in luck. The field is rapidly expanding and ever-evolving, especially as data affects and influences most aspects of our daily lives.

If you’re interested in working in data science, it’s important to keep up with the latest trends in the field leading into the future. New technological developments are always on the rise, and there are always plenty of career opportunities on the horizon.

In this article, we’ll explore:

• The different professions of data science and the most appealing jobs of the future

• The different factors that influence the direction of future data science jobs

• The skills that are important for a career in data science.

Let’s dive right in!

The Current State of Data Science Jobs

Currently, there are three key subfields in data science that the majority of jobs will fall under the category of. Employment in each subfield is high and projected to grow even more from now until 2030.

These subfields are each focused on different niches, such as the collection, analysis, modeling, and reporting of data. Let’s take a quick glance at each of these key roles:

Data Analysts

Data analysts are one of the most prominent professions in the field. They gather data from databases, organize it, and study that data to make findings related to the subject at hand.

Through collection and interpretation, data analysts arrive at conclusions on what the data is expressing. With these insights, they’re in a prime position to solve problems and help businesses become more secure.

Data Scientists

This role is often confused with that of a data analyst. However, being a data scientist requires a more solutions-based approach – which often involves the development of new models and methods of analysis.

Data scientists are often employed by an organization to seek out trends and advise on business strategy.

Data Engineers

Data infrastructure that processes raw data material and turns it into something comprehensible is designed and maintained by data engineers. These professionals set the stage for data scientists and analysts to do their job efficiently.

To create and manage these systems, data engineers require strong programming skills and must be proficient in a variety of programming languages.

Factors Influencing Future Data Science Jobs

The future of data science is bright and ever-evolving. Technological advancements and the increasing popularity of data use within businesses are key to future trends in data science.

Technological advancements

With the rise of AI technology, machine learning, and cloud computing, skilled employees are becoming better at working with data.

These advancements allow businesses to achieve more effective and efficient data analysis at an increasingly affordable rate.

Data explosion

Every day, regular people create an enormous amount of data through channels such as Facebook and Google. At the same time, most businesses today utilize data for more effective strategizing.

With this ever-increasing generation of data, there’s also a growing pool of jobs available to those who can process and interpret such data.

Many businesses employ their own data analysts, scientists, or engineers to manage and interpret the data that is vital to the effective running of their operations.

Data privacy and ethics

Rapid technological development has made discussions about how to responsibly protect and use data even more important.

Simply put: data ethics and rules around data privacy can be very tricky to navigate!

Many businesses fail to consider these factors, resulting in the breach of acceptable standards.

Because of this, there’s an increasing need for data privacy and ethics experts who are highly knowledgeable of these standards and skilled across multiple disciplines.

6 Top Future Data Science Jobs

The pool of data science jobs is already large and is forecasted to grow by 36% by 2030.

Data scientist job growth - source

At that point, technology such as AI may begin to decrease the need for human involvement in data science.

Until then, the use of AI is bringing about entirely new careers within the data science field, such as those discussed below:

1. AI Ethics Officer

The use of artificial intelligence involves many ethical implications.

Many businesses are now opting to employ AI ethics officers who monitor and guide the use of this technology to ensure that ethical standards are always met and upheld.

In this role, it’s vital to strike a balance to make sure that the use of AI is serving the interests of consumers, citizens, and the company properly. Learn more about the ethics of AI in a separate article.

2. Data Translator

Data translators are the link between raw data and decision-making.

These are experts in data collection, interpretation, and modeling and are capable of thoroughly understanding the more technical aspects of data science.

Not only that, but they’re also skilled at business strategization. Data translators inform decision-making by applying their wide range of skills to make sure that the business is functioning as effectively as possible.

3. Data Privacy Expert

Not only must consumer and citizen data be protected, but a business’ own private data is just as vital. For this reason, businesses are hiring data privacy experts to advise on data security.

A data privacy expert ensures the safety of sensitive information belonging to staff, customers, and clients.

Knowledge of data protection practices and laws is a must for this role, as well as strong knowledge of IT and relevant software.

4. Database Administrator

As companies grow, they accumulate information that must be organized to keep them efficient and secure.

Database administrators are the people responsible for managing databases and making sure that these are always available.

The smooth operation of your database guarantees that the right information is always accessible at any time.

They’re also the ones responsible for drafting and enforcing policies related to the use of a database so that its integrity remains untarnished.

5. Machine Learning Scientist

This role is less focused on the data itself and instead puts increased emphasis on the algorithms that use data or the software engineering used to process that data.

The specific tasks that a machine learning specialist may be faced with are rather varied, though they tend to be heavily research-based.

To succeed in this role, a candidate would require skills in model deployment as well as a deep knowledge of artificial intelligence. Learn more about machine learning and how to start your career in the field in a separate post.

6. Machine Learning Engineer

This role is focused on the creation of algorithms and data sets. It’s also a profession that’s closely linked to that of a machine learning scientist. However, machine learning engineers are more practical and less research-based.

Development of algorithms, as well as testing and maintenance, are key tasks within this role.

As such, education in computer science and software engineering is necessary to succeed as a machine learning engineer. Discover how to become a machine learning engineer in a separate post.

Skills Needed for Future Data Science Jobs

With the forecast of future trends in data science being largely AI-oriented, you’ll find that some of the skills in this section are geared toward ensuring you remain relevant in the field and enjoy longevity.

Other skills here are more familiar ones that you’ve always had to include on your cover letter when applying for data science jobs:

• Software engineering and coding. Proficiency in coding languages like Python, as well as data engineering and modeling, are necessary skills for many careers within future data science jobs.

• Data analysis. Being able to identify a need – as well as the relevant data to fulfill that need – is a skill that’s required by most data science jobs. This is true of today’s roles and those of the future. Learn more about data analysis in this DataCamp course.

• Verbal and written communication. Being able to effectively report on your findings both verbally and in written form is a requirement of almost every role within the data science field.

• Knowledge of data privacy laws. Data privacy is becoming increasingly important as the amount of data produced every day continues to grow. Public awareness of data privacy is growing as well, increasing the need for businesses to abide by data privacy laws. Expert advice is generally necessary for this area.

• Computer science. Knowledge of computer science is essential so that you’re able to create systems that effectively process data. Machine learning engineers are one of the roles that will require knowledge and mastery of computer science.

• Knowledge of ethics. Knowledge of ethics is vital to the ethical collection and evaluation of consumer and citizen data. The use of data has great potential to both positively and negatively impact people’s lives. Businesses dealing in data must seek expert advice on how they can do so ethically.

Final Thoughts

The nature of data science jobs is constantly changing and adapting, as we’ve seen with the rapid emergence of artificial intelligence technology.

The fluidity of this technology has reinvented data science and the way people work in the field.

New careers in data science are popping up with this evolution, even in spite of fears that we may see an AI-induced tide change by 2030.

What remains obvious right now is the importance of continuous learning and adaptability for those who want to remain relevant in the data science field!

Get started with your career today with DataCamp’s Data Scientist with Python career track, which covers all the essential skills you need to succeed as a data scientist.