How to Become a Data Scientist (Duties, Salary and Steps)

Are you considering a career in data science? Learn everything you need to know with our insightful step-by-step guide!

Illustration of a woman working on her a computer and holding a magnifying glass

Data science is becoming a big business. As organizations become more dependent on data, metrics, and numbers, they are starting to collect this information in new ways and with greater prevalence. The specialized process of making sense of such large amounts of data and using it to make well-informed business decisions has helped the demand for data scientists surge in recent years. 

If you are interested in becoming a data scientist, then read on to find out the main responsibilities of a data scientist, salary information, and how to pursue this fascinating career path.

What data scientists do

Data scientists are reasonably new kids on the block, taking huge sets of data, which can be both sorted and unsorted, and analyzing it for trends and meaning. Their role involves converting this data into comprehensive reports for organizations, which they can then utilize and implement within their strategy.

Data scientists draw upon different skills to perform this complex role. They need to use mathematics, information technology, statistics, industry knowledge and socio-economic theory to make sense of complex information and influence leaders in developing meaningful business solutions.

The role of data scientists might look different across separate organizations and industries, but the following is a list of what the job generally involves:

  • Developing systems and algorithms to efficiently export data and present it to stakeholders
  • Develop statistical techniques and use machine learning to develop solutions to problems
  • Communicate data, results, and analytics clearly to facilitate people’s understanding of complex information
  • Create detailed, logical, and engaging reports that will bring data and findings to life
  • Work with areas which don’t effectively use data, developing ways to analyze information
  • Research different ways to collect data, determining which are best for your needs
  • Staying on top of data science trends and techniques
  • Influence and negotiate with organisational leadership
  • Recruit, train, and motivate a team of junior scientists

What the job is like

Data science is typically an office job, so you can expect a reasonably normal work-life balance. Nevertheless, there are some nuances to the role you should be aware of before embarking on this career.

Work environment

Data scientists are often office-based but may also work from home or undertake hybrid working. As the role involves delivering and interpreting complicated data to organisational leaders, there might be a need for frequent face-to-face meetings. Therefore, a reasonably high degree of presence will also be needed. 

Unless the specific data science role is field based (e.g., working for some governmental organizations, law enforcement or roles requiring overseas deployment), your place of work will be an office.

You might need to spend long hours in server rooms, which are kept cold. Therefore, there might be a small risk of exposure to low temperatures. The role can be influential and reasonably senior, so there will be associated stressors which come with operating at this level.

Work hours

Data scientists can have a good work-life balance, beginning work around 8am and finishing around 6pm, Monday to Friday. There might be extended hours from time to time, and ad-hoc project work requiring longer shifts, but most data scientists can expect to receive this back as overtime or time in lieu. More senior data scientist roles might have to work longer, in line with other members of leadership.  In these cases, there might be an expectation to stay as long as is needed to get the work done.

Job satisfaction

Data science offers high job satisfaction levels compared to other related fields, and ranked second in Glassdoor’s list of top jobs in the US for 2021, only behind Java developer. The reasons for this are a happy mix of factors, including decent work-life balance, high earning potential, high engagement and influence at work, and better socialization opportunities compared to other technology jobs.

Job market

The combination of rising salaries and a popular profession often leads to an oversaturated job market.  Data science certainly fits this bill, but the immediate term outlook is positive for jobseekers. Burtch Works research shows that 81% of data science teams were recruiting for roles at the end of 2021.

The world is using more and more data. As different industries and companies undergo greater, and more urgent, change to adapt, data science continues to be in more demand than ever before. Companies which have never hired data scientists are starting to create job openings and even kickstart whole new divisions to benefit from their expertise.

Like many technology jobs, the data science market is becoming international, not just local. Firms wanting to hire top talent can now recruit data scientists from anywhere in the world, thanks to the ease of remote working

Another consideration is that whereas data science is a popular, and dare we say it, ‘trendy’ field, the initial glut of people trained and ready to take jobs has passed. Organizations are competing heavily for a finite supply of qualified data scientists. This not only creates earning power for employee, but also means that data scientists can be lured by a competitor for more money.

Salary

The technical, specialized nature of data science commands people who are well-qualified and equipped with a wide variety of skills. This, along with demand for data scientists outstripping supply, means that salaries in this field are competitive. This section takes a look at data science salaries in more detail.

Mean wage

To illustrate the earning power of data scientists, the mean wage for this role is 84% higher than the US national average identified by the US Bureau of Labor Statistics at $56,310.

Mean annual wage

Mean hourly wage

$103,930

$49.97

Median wage by experience

The earning potential of data scientists increases significantly as they become more experienced, with top level data scientists earning over three times what an entry level worker will make.

Experience

Median annual wage

Entry level

$52,950

Junior level

$71,790

Mid level

$98,230

Senior level

$130,370

Top level

$165,230

Mean wage by state

This table shows the top five states in the US offering the highest mean annual salaries for a data scientist.

State

Mean annual wage

New Jersey

$116,250

North Carolina

$117,370

Washington

$118,320

New York

$124,240

California

$129,060

Median wage around the world

Outside of the US, data scientists also enjoy high median annual salaries. It is worth noting that many countries expect these salaries to increase significantly. For instance, the UK job market expects to see average salaries double in the near term.

Country

Median annual wage

Australia

AU$92,370 ($66,190)

Canada

C$79,880 ($63,300)

Ireland

€44,140 ($49,810)

New Zealand

NZ$73,560 ($49,190)

UK

£40,770 ($55,050)

Steps to become a data scientist

So, if you have read through the above and have decided that data science is the industry for you, how do you get started? Becoming a data scientist takes a fair amount of preparation and upskilling, either through education or as part of retraining. This section takes you through what you might need, and how to become a data scientist.

1. Determine if it’s the right job for you

Data science is such a new field you might be wondering whether it is the right avenue for you to go down. When finding the right career, a good place to start is to see if there is alignment between the job and you as a person.  Here are the main the skills and qualities needed for success in data science:

If these skills pique your interests, or are aligned with your personality, or existing work responsibilities, then you’re off to a good start. Data science draws upon specialized skills and an unusual mix of qualities, and if you are not sure you have the right combination, then don’t fear.

Career tests, such as our own state-of-the-art CareerHunter test, can help you understand what kind of jobs suit you. Rarely will there be a perfect fit but look for the best matching career according to who you are.

Another good way to determine if this career is for you is to take time to research the data science profession. Connect with data scientists on LinkedIn or contact companies to learn more about the role.  This is also a great way to show interest and build your professional network.

2. Focus on the right subjects at school

Like many careers, a foray into data science often begins at school by choosing the right subjects.  The best subjects to start on would be computer science or information technology (IT), as well as mathematics, including any specialisms such as statistics.

Find any excuse to learn more about IT – for example, if your school offers coding workshops or classes on AI, then sign up for these too. Other subjects that might help would be English (to brush up on communication skills) and business studies.

3. Learn to code

You might not be able to learn coding at school and picking up this skill at any stage of your life can seem daunting. There are plenty of ways to learn how to code which are inexpensive and sometimes even free. 

The first step is to focus on the right programming language as this will align with what you can do and meet the needs of future employers. Websites with highly structured databases will require knowledge of Java or Python, so these are two good places to start. Next, research online courses to get an introduction to coding. If you gel with coding, consider taking professional courses and certifications too so you can enhance your credentials and CV.

4. Get a bachelor’s degree

If you are leaving school and considering a move to higher education to train as a data scientist, there are plenty of bachelor’s degrees options you can consider. Computer science, for one, is widely available in most academic institutions and is a good foundation for your career. Another good degree would be mathematics; specifically, statistics would be perfectly aligned to the role. 

Many data scientists choose physics, because of how it draws upon several different relevant areas such as mathematics, problem solving, analysis and programming. Economics or business degrees also have relevance to the results side of data science.

5. Complete a master’s degree

Obtaining a master’s degree might not be an absolute prerequisite to start a career in data science but it does make you a lot more attractive to recruiters. A master’s in any field of analytics would be useful, as would qualifications in AI or machine learning. Meanwhile, a master’s degrees in applied mathematics would give you specialized knowledge which would be invaluable to data science.

6. Earn a professional certification

Coding certifications will go a long way, as well as certifications in AI and machine learning. There are many certifications available at your fingertips offered by prestigious organizations, such as IBM and Google. 

Finally, look for certifications which combine several data science skills, such as mathematics for machine learning. Many of these courses are available on Coursera or similar, and whereas they will have costs attached, can add a whole new dimension and added credibility to your job applications for data scientist roles.

Final thoughts

Data science can be a hugely rewarding and fascinating career. It allows you to draw upon a wide array of skills and technical knowledge and contribute to meaningful, evidence-based decisions in a business.  

The sheer amount of data and analytics being used by organizations means that data science is here to stay. As such, this role not only offers a high personal satisfaction, but it also has great earning potential too.

Data scientists might be in high demand but that doesn’t mean it’s easy to step into the industry.  Becoming a data scientist requires vigorous training, a drive to learn and a keen eye for detail. So, if you think you have what it takes, all that is left to do is set a personal development plan that will give you a foothold in this competitive industry.

Join the discussion! Are you an aspiring data scientist? What do you like the most about this career path? Let us know in the comments section below!