Data scientist Job Profile

What does a data scientist 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

Wondering what a data scientist is? Anchor Are you looking for a career investigating the truth behind the data, or 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?

A data scientist takes raw data – that is, information given to you by a client or company – and spends time looking for trends and making sense of the data. This is so that they can interpret the reason behind the trends they see. They then report back to the company about what the data shows and suggest future decisions that could help to improve 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 they company should take. 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.

While there are many sectors that you can work in, there are certain things a data scientist will do in most or all of these. These include:

Having meetings with your team to receive work and discuss the work you are doing
Asking questions to fully understand a brief
Cleaning the data given to you to analyse
Analysing the data to try and observe trends or patterns using coding languages and software
Working on algorithms and testing them to assist your analysis
Building models to help predict future trends
Presenting 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 either as 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 around 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 tasks similar to those of 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 around seven years of working as a data scientist. Responsibilities of senior data scientists include determining their own work based on project 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 does the most complex work Anchor 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

What a data scientist is likely to earn 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. Here are the salary levels a full-time data scientist could expect:

When your career begins as either a junior data scientist or in an entry-level data scientist position, you could make around £35,000 per year
In a mid-level data scientist job, you could make around £60,000 per year
A senior data scientist's salary will be between £70,000 and £85,000 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 to meet to be a data scientist:

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 do 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 more quickly in your career or make the transition from education to work easier.

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

Professional qualifications

Gaining professional qualifications helps you become a data scientist by demonstrating your skills and giving you more experience. Anchor This is to make you stand out among 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
Excellent mathematical and statistical skills for model building and understanding how to effectively analyse data
Understanding of 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.
Understanding of 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 kickstarting your 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 – either in full-time, permanent employment with job security, or in contract positions taking on interesting and new challenges of your choice
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) can be difficult and time consuming

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 can do as a data scientist, your working week could increase to around 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:

Related jobs

This article was last updated in February 2025.

Data Scientist