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Data scientist

Find patterns in raw data to help solve problems or make predictions.

About skills Getting in

About the job

Salary

Source: National Careers Service

Weekly

£615

Entry level

£1,587

Experienced

Monthly

£2,667

Entry level

£6,875

Experienced

Yearly

£32,000

Entry level

£82,500

Experienced

5,500

people are currently employed

High growth

300 more jobs in 5 years

These figures refer to this job and similar ones with comparable skills and qualifications. They only apply to Scotland. Source: Oxford Economics

Career insights – Data scientist

What it's like

Businesses use data analytics to improve decision making. It’s how organisations get a better understanding of their customers or work out product trends.

For example, think about flight prices. Airlines put prices up during busy times when lots of people want to fly. During quieter times, they lower them to attract customers. Data science helps inform these decisions.

You’d collect and analyse large amounts of raw data from a range of sources like surveys, analytics or cookies on a website. Then you’d look for patterns or insights using different methods. For example, algorithms, data mining, artificial intelligence, machine learning and statistical tools.

You’d then present your results to help make predictions or find solutions to problems.​​​​

Things you might do

  • identify issues or opportunities to use data in an organisation

  • collect raw data and turn it to a useable format

  • look for patterns in the data to help identify trends

  • test data mining models to select one that suits the need

  • solve business related problems using machine learning tools and statistical techniques

  • present your results to your organisation or client

  • stay on top of analytic techniques like machine learning and text analytics

  • work with a variety of programming languages, including 'SAS', 'R' and 'Python'

You could work in a variety of industries. Depending on your interests and the opportunities available, you could work in:

  • finance

  • fintech (financial technology)

  • academia

  • scientific research

  • health

  • retail

  • information technology

  • government

  • ecommerce

"Data science was seen as nerdy but now it's creative. Communication matters because you have to sell what you uncover and that means telling stories around data. The future belongs to people with soft skills just as much as to those who master hard data analysis."

Euan Davis

Data Scientist, Cognizant

Hours

Working hours can vary, but usually full-time hours will be Monday to Friday and around 37 hours per week. Some jobs or projects might require you to work longer hours or weekends.

Environment

You'll usually be office based and working at a computer most of the time.

Travel

Some travel may be required, depending on the role you're working in.

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Related industries

Many jobs can be done in lots of different industries. We've highlighted the ones we think are most important for this job.

  • Engineering and manufacturing
  • Construction and built environment
  • Life sciences
  • Financial services
  • Digital technologies
  • Creative
  • Chemical sciences
View all industries

Top skills

Skills are things you're good at. Whether you know what yours are or not, everyone has them!

It's useful to learn which ones are important in a job so you know the areas you need to brush up on. It can also help you work out if you're suited to a career.

Here are some of the skills you'll need to do this job:

  • analysing
  • making decisions
  • developing a plan
  • attention to detail
  • researching
  • problem solving
  • creative
  • written communication
  • verbal communication
  • cooperating

Your skills are important

Our unique skillsets are what make us stand out from the crowd. Learn about each skill in depth and discover what employers look for in your applications and interviews.

Discover skills

Getting in

Explore the sections shown for more information about getting into this career.

You might have qualifications which are not shown here but will allow you access to a course. You can compare your qualifications by looking at their SCQF Level. For more information about this, check out the SCQF website.

Always contact the college, university or training provider to check exactly what you'll need.

Colleges and universities will list subjects you'll need for entry to a course. Some useful subjects include:

  • Computing Science

  • Mathematics

  • Foundation Apprenticeship: Hardware and Systems Support

  • Applications of Mathematics

You can get a head start in this career by doing a Foundation Apprenticeship in S5 and S6.

You'll get an SCQF level 6 qualification which is the same level as a Higher. You'll also learn new skills and gain valuable experience in a work environment.

Discover what's on offer at your school on  Apprenticeships.scot.

Entry requirements for courses can change. Always contact the college, university or training provider to check exactly what you’ll need.

To become a data scientist, you'll usually need a degree in a computer science, mathematical or science-based subject. The following degree subjects may be particularly useful:

  • computer science

  • statistical data science

  • data science

  • mathematics and operational research

  • statistics

  • maths

You'll also need experience of computer programming and strong coding skills. An understanding of data collection and pattern recognition is also important.

Qualifications are available at postgraduate level. If you don’t have a related degree, you could take a postgraduate qualification in a relevant subject area. For example, in big data, business analytics or data science.

Not all employers list specific qualifications you need. They do ask for relevant experience that demonstrates a range of transferable skills. This is usually work based.

It's also helpful to have experience in the use of the programming language 'Python'. Experience with database interrogation and analysis tools, such as 'Hadoop', 'SQL' and 'SAS' would also be useful.

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