How to Become a Data Scientist (Career Path)

data scientist

If you’re analytically minded, and enjoy working with numbers, becoming a data scientist could be a wise career move. Although predominantly employed in the financial sector, their services are becoming increasingly sought after in a huge variety of industries and fields.

If you are considering this career path, this guide could come in handy!



1. Research the Profession

Before you decide on a career, you should always research it thoroughly. This will allow you to get a clearer picture of your chosen profession, as well as giving you an idea of how to get involved.

Job Description

As the value and importance of data begins to dawn on businesses and organisations, data scientists are required to use algorithms and statistical techniques to turn that data into information.

It is not just a case of having technical know-how though. Data scientists are required to have a knowledge of the industry they are operating in, so that they can make sense of that information and understand what is important and what isn’t. And after that, they need to be able to explain their findings clearly and effectively to others; communication is a key skill in data science.

Key Responsibilities

Depending on the organisation you’re working for, your role may vary slightly, but generally the responsibilities of a data scientist are as follows:

  • Work with other departments in your organisation to identify issues, and use data to propose effective solutions
  • Merge, manage, and extract data to create tailored reports for colleagues, customers or the wider organisation
  • Maintain clear communication with the organisation at all times to ensure data needs are understood and met
  • Use machine learning tools and statistical techniques to provide solutions as required
  • Create clear and concise reports that offer value to customers or the business
  • Stay up to date with the latest technologies, techniques and methods
  • Conduct research into prototypes and proof of concepts
  • Seek opportunities to use insights, codes or models that could benefit other functions of the organisation (i.e. HR or marketing)
  • Promote education of data science and encourage others within the organisation to see the benefits of your work

Essential Skills & Qualities

  • Very strong communication skills, to explain complex concepts to people who have no working knowledge of the mechanics of data analysis
  • Meticulous attention to detail and the ability to problem solve effectively
  • Experience with (or a willingness to get to grips with) database interrogation and analysis tools such as SQL
  • Self-motivation and the ability to work unsupervised
  • Good organisational and planning skills
  • A collaborative approach to sharing ideas and finding solutions, as you will be required to work with other departments

Working Hours and Conditions

This will vary depending on the organisation you work for, but you can reasonably expect to work normal Monday to Friday office hours. If you have deadlines, you may be required to work longer hours or on weekends.

Salary Prospects

In the UK, most entry-level positions offer starting salaries of between £19,000 and £25,000. As you gain more experience and seniority, this can rise to anywhere between £30,000 and £50,000, with high-level scientists and consultants able to command salaries of anywhere between £60,000 and over £100,000.

In the US, starting salaries are around $65,000, a figure which can rise all the way up to $135,000. The average salary is around $90,000.

These figures are variable depending upon the type of industry you are working in (for example, financial firms tend to pay higher end salaries), and the location you are working at.


20 percent discount
20 percent discount


2. Get the Qualifications

Typically most companies will require you to have a degree in data science or a related field, but it doesn’t necessarily have to be in a computer or science-based field. Strong quantitative skills are of course important, but being able to solve problems logically and methodically are bigger factors.

That said, it is important to have some technical skills. Knowledge of programming languages – especially Python – is an absolute must, as you will be handling huge amounts of data, and realistically most companies will look for familiarity with other coding languages and software programs.

If you are changing career, studying for postgraduate qualifications in a relevant field might help, but these aren’t necessarily requirements. Some good subjects to focus on would be:

  • MSc Data Science
  • MSc Business Analytics
  • MSc Data Science and Analytics
  • MSc Big Data

3. Land Your First Job

Data science is a hugely in-demand profession at the moment, as organisations begin to realise the importance of using their data to make informed decisions. As a result, companies in every industry are on the look-out for talented and knowledgeable recruits, with businesses competing against each other to secure the very best talent.

If you require more experience, many larger companies offer internships and work-shadowing programs where you can put your knowledge into practice and build up a professional network of contacts.

There are also online competitions you can enter, such as those hosted by Kaggle, Topcoder and the Defence Science Technology Laboratory (DSTL), where recruiters are often on the lookout for new and emerging talent.

Some of the more prominent industries you could work in are:

  • Finance
  • Academia
  • Scientific research
  • Retail
  • Information technology
  • E-commerce

This list is not exhaustive though. In recent years, data scientists have become a valuable asset in telecommunications, transport and energy companies – essentially any industry where companies generate data.

As jobs are in such high demand, keep an eye out on job listing sites, or if there is a particular industry that you want to work in, research the companies in that field and check their websites regularly for positions. Alternatively, you can try these sites:

4. Develop Your Career

In terms of professional development, there is no real accreditation or certification available. You may be asked to attend industry specific training courses to broaden or expand your knowledge though, as well as being encouraged to stay up to date with emerging trends and developments within data science.

In regards to career development, a lot depends on how long it takes you to learn the necessary skills to analyse large sets of data and present your findings effectively. There are several steps on the promotion ladder, as most companies have senior data scientists; in this role, you would take on additional management tasks and be responsible for a small group of junior data scientists.

As the skills you will learn and possess are not confined to one particular industry, it is relatively simple to move to different companies or to work overseas.

Job Outlook

The job outlook for data scientists is hugely positive. The UK government claims that 56,000 data scientist jobs will be created each year until 2020, while management consultancy experts McKinsey & Co predict that in 2018, there will be between 140,000 and 190,000 data science positions going unfilled. With talent in such short supply, companies are increasingly willing to pay top price to secure the right skillsets.

In the US, the demand is similar. The Harvard Business Review (HBR) claim that the shortage of data scientists is becoming a “serious constraint” in some sectors, declaring data science as the “sexiest job of the 21st century”. Additionally, it was voted the best job of 2017 on careers site Glassdoor, with an average rating of 4.8 out of 5 – high praise indeed.



This is probably the golden age for data scientists, as they are definitely operating in a buyer’s market. With the dangling carrot of high incentives and a flexible and resilient skillset that provides strong job security, now has never been a better time to pursue a career.

Do you work in data science? If so, let us know your experiences in the comments…




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