Data Scientist

• What do data scientists do?
• Data scientist career path
• Data scientist salaries
• Qualifications and training
• Data scientist skills
• Pros and cons of being a data scientist
• Data scientist work life balance
• Data scientist employers
• Related jobs
• More information

Are you looking for a career investigating the truth behind the data? Do you enjoy finding patterns where others can’t? If you have great analytical skills, a career as a data scientist could be for you.

If you’re interested in a career as a data scientist, explore IT and software jobs available now.

What does a data scientist do?

As a data scientist, you take raw data which is information given to you by a client or company and spend time looking for trends and making sense of the data. This is so you can interpret the reason behind the trends that you see. Once you understand this, you report to the company about what the data shows and suggest future decisions that they can make to improve their efficiency.

Many types of organisations need data scientists. You could work with the financial sector dealing with anything from fraud detection to financial risk management. You could work in the retail sector analysing data to understand the trends in sales and advising on which route to take the company. You could even work in healthcare using your statistical knowledge to understand how a disease spreads, determine the most efficient treatment methods for a disease or illness and maybe even help to prevent an illness. 

Whilst there are many sectors that you can work in as a data scientist, there are several responsibilities that you do in most or all of them.

• Have meetings with your team to receive work and discuss the work you are doing
• Ask questions to fully understand a brief
• Clean the data given to you to analyse
• Analyse the data to try and observe trends or patterns using coding languages and software
• Work on algorithms and test them to assist your analysis
• Build models to help predict future trends
• Present your results to your manager or a company’s management

Data scientist career path

Your career could be long and varied if you become a data scientist. Here is the typical career progression from entry-level to senior positions for a data scientist:

Entry-level

You start as either a junior data scientist or in an entry-level data scientist position. These roles are designed to introduce you to the field and give you the necessary training for the work. 

In an entry-level position, you use your existing understanding of coding languages to build models and write programmes to answer questions that a company may have with its data. Your work is receiving a task and working it through until you produce an answer rather than deciding on the best course of action for your work or your team.

Career progression

After 1-2 years of experience, you progress to a mid-level data scientist position. This gives you more responsibility in your daily tasks. You build your own programmes and models to deal with data. You have more freedom to work without supervision and complete similar tasks to colleagues in less senior positions but to a higher level and without a supervisor assigning you work.

Senior data scientists have even greater responsibility than mid-level data scientists. You could achieve this level within seven years of working as a data scientist. Responsibilities of senior data scientists include determining their own work on a project based on its requirements, providing training and help to junior and entry-level data scientists and designing systems and programmes.

Future career

Beyond senior data scientist, you can become a principal data scientist. This is an executive position that gets the most complex work and may even identify opportunities for new companies or clients. 

Alternatively, you could progress to a director position. This work involves making large decisions for the less senior members of the team, setting the culture for the team and being accountable for the work of everyone on the team.

Data scientist salaries

The amount of money you get paid as a data scientist depends on the industry you work in and your level of employment. It can also depend on whether you work in a contract position which means working on a project for one company and moving on when it’s done, or if you’re a full-time, permanent employee for one company. If you’re a full-time employee, here are the salary levels that you could expect:

• When your career begins as either a junior data scientist or in an entry-level data scientist position, you could make between £27,000 and £35,000 per year.
• In a mid-level data scientist job, you could make between £35,000 and £47,000 per year.
• As a senior data scientist, you could make between £64,000 and £75,000 per year.

Qualifications and training 

The demand for data scientists reflects the high skill and education level that you need to succeed in the job. Here are the requirements you need before entering the profession:

Education

Your path to becoming a data scientist begins with an undergraduate degree. This is typically in data science or another computer-based course like computer science. After finishing an undergraduate degree, most data scientists work towards a master’s degree in data science. Becoming a data scientist doesn’t usually require a PhD. However, to reach executive or director positions it is preferable to have one. 

Work experience 

Having work experience in data science could help you progress quicker in your career or make the transition from education to work easier. 

Some degree courses include a work placement scheme or time in the industry where you work for a company for some time gaining experience before finishing your degree. You could volunteer in a data science company or complete an internship during or after your studies.

If you’re interested in a career as a data scientist and need work experience, why not explore internship opportunities?

Professional qualifications

Gaining professional qualifications helps you become a data scientist by demonstrating your skills and giving you more experience. This is to make you stand out amongst the other people applying for jobs by proving the skills you say you have in your CV.

If you’re interested in professional qualifications to increase your success, consider the CAP certification and the IBM Data Science Professional Certification.

Data scientist skills

Here are some hard and soft skills that you need to succeed in your career as a data scientist:

Hard skills

• Working knowledge of coding languages like Python so you can generate algorithms and make predictive models. Take our Bright Network Academy course on starting to code in Python to kickstart your skills in this language
• Excellent mathematical and statistical skills for model building and understanding how to effectively analyse data
• Understand big data. You work with enormous datasets so understanding how to clean the data, process it and build algorithms to analyse is key to succeeding as a data scientist
• Understand machine learning and AI including deep learning and natural language processing

Soft skills

• Analytical skills. Data scientists need good analytical skills to effectively recognise a pattern in the big data
• Communication. Being able to effectively communicate is important for data scientists so you can translate the complex, technical trends that you find in data into simple, easy to understand language for non-technical managers and colleagues
• Curiosity and a desire to learn. Staying at the top of a data scientist job means staying up to date with innovations in machine learning, computer science and maths. Updating your knowledge of these fields through reading, speaking to colleagues in other areas of work and attending conferences helps the work that you do stay relevant and appealing to companies.
• Research. Having great research skills helps you to understand why a client or company needs a data scientist. You can research the previous trends that they’re interested in which helps you identify whether the trends you see in the data are typical of the company or industry and whether they need further investigation.

Pros and cons of being a data scientist

Here are a number of things that you should consider before jumping into a career as a data scientist:

Pros

• There are lots of data scientist jobs available
• No two days are the same; your work is varied and changes on a daily basis
• You could help save lives if you work for the healthcare sector
• You have freedom to choose how you want to work in either full time, permanent employment with security or in contract positions taking on interesting and new challenges that you choose
• Large and well-known companies need data scientists, giving you the chance to work for household names
• There is wide scope for changing the sector you work in if you don’t enjoy the work
• It’s challenging work with new puzzles for you to work on

Cons

• The job often involves high volumes of work which means working overtime
• You need high-level qualifications to get into a career as a data scientist
• Keeping on top of advancements in all the areas that you need to work well as a data scientist (including statistics, computer science and maths) is hard
• You need to be a master of many different areas to be a successful data scientist which is difficult to achieve

Data scientist work-life balance

Data scientists typically have a full working week from 9am to 5pm, Monday to Friday. Due to the high volume of work that you do as a data scientist, your working week could rise to up to 60 hours, particularly around deadlines or when finishing a project. 

Data scientist employers

As a data scientist, you could work with some of the biggest companies in the world. Many of them have graduate schemes to train new graduates. Here are some top companies that hire data scientists:

• Apple
• Amazon
• Facebook
• Microsoft 
• Google
• Oracle
• Accenture
• Slack
• National Grid
• Tui

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More information

Are you interested in a career in data science? Learn about how Bright Network member Nick progressed to senior data scientist with Ofcom.