Data scientist

Data scientists turn raw data into meaningful insights that organisations can use to improve their decision-making and operations

As a data scientist, you'll extract, analyse and interpret large volumes of data from various sources, using algorithms, data mining, AI, machine learning, and statistical tools to make it accessible and useful for businesses. Once you've interpreted the data, you'll present your findings in clear, engaging, and actionable formats.

You'll use your technical, analytical and communication skills to collect and examine data, helping organisations identify patterns and solve problems. This might involve predicting customer behaviour or addressing environmental challenges such as plastic pollution.

Types of data scientist work

You can work across a range of industries, including:

• finance
• academia
• scientific research
• health
• retail
• information technology
• government
• ecommerce.

Responsibilities

As a data scientist, you'll need to:

• work closely with stakeholders to identify problems and use data to develop effective solutions
• build algorithms and design experiments to merge, manage, interrogate and extract data for tailored reporting
• use machine learning and statistical techniques to solve problems
• evaluate and select the most appropriate data mining models for specific projects
• communicate complex data insights clearly and effectively to both technical and non-technical audiences
• create compelling reports that tell the story of how customers or clients interact with a business
• assess and enhance data sources and data-gathering methods
• stay informed about emerging technologies and methodologies
• conduct research to develop prototypes and proof-of-concept solutions
• identify opportunities to apply data insights or models in other business areas, such as HR or marketing
• maintain a proactive and curious mindset when using algorithms to solve challenges and inspire others about the value of data science. 

In senior roles you'll also need to:

• recruit, train and lead a team of data scientists
• develop and oversee the organisation's data science strategy
• implement new systems and improve data workflows
• assess and adopt emerging technologies
• represent the organisation at external events and conferences
• build and maintain client relationships.

Salary

• Salaries for junior data scientists generally start at £25,000 to £30,000, rising to £40,000 depending on your experience.
• With a few years' experience you can expect to earn between £40,000 and £60,000 in a data scientist role.
• Lead and chief data scientists can earn upwards of £60,000, and in some cases £100,000+.

Data scientists contracting out on short-term projects tend to earn between £450 to £500 per day.

Salaries vary depending on a range of factors including your experience, qualifications, location and the sector you work in.

Benefits can include a company pension scheme, flexible or remote working, performance bonuses and private medical insurance.

Figures are intended as a guide only.

Working hours

Depending on the type of company you work for, you can expect a good work/life balance. Core office hours are typically between 8am to 6pm, Monday to Friday. There may be times, particularly on short-term projects, where working outside of core office hours or at weekends is necessary.

In some organisations you may have the opportunity to work remotely or on a flexible schedule.

What to expect

• Data science is a collaborative field, with many people sharing their methodologies and insights, so you should be prepared to share your ideas and solutions with your wider team.
• Jobs are available in towns and cities throughout the UK with companies in a large range of sectors. There are also opportunities to work overseas.
• Roles are usually office-based, and a large proportion of your time will be spent at your desk. You'll be encouraged to learn as much about the business as you can to help identify solutions to problems.
• Women are currently underrepresented in data science, although initiatives such as Women in Tech are working to redress the imbalance.

Qualifications

You'll typically need a degree in a computer science, mathematical or science-based subject to work as a data scientist. The following degree subjects may be particularly useful:

• computer science
• data science/computer and data science
• engineering
• mathematics
• mathematics and operational research
• physics
• statistics.

Knowledge of programming languages such as Python, R, SQL, C or Java, and strong database and coding skills, is essential.

Some employers offer graduate training schemes, often lasting around two years. These can be competitive and may accept graduates from various disciplines depending on the programme.

A postgraduate qualification (e.g. MSc or PhD) in a relevant area can be helpful, especially if changing careers or building advanced analysis skills. Relevant postgraduate subjects include:

• big data
• business analytics
• data analytics
• data science.

You'll usually need a background in mathematics, engineering, computer science or science. However, degrees in business, economics, psychology or health can also be relevant if you have mathematical aptitude and basic programming knowledge.

You can also enter the profession through apprenticeships, offered by organisations such as Dstl (Defence Science and Technology Laboratory) and other major employers.

Search postgraduate courses in data science.

Other relevant subjects at postgraduate level include machine learning, mathematics, physics and computer science.

Skills

You'll need:

• excellent analytical and problem-solving skills
• experience in database interrogation and analysis tools, such as SQL, Apache Hadoop or SAS
• strong communication and presentation skills for explaining your work to people who don't understand the mechanics behind data analysis
• effective listening skills for understanding the requirements of the business
• drive and the resilience to try new ideas if the first one doesn't work - you'll be expected to work with minimal supervision, so it's important that you're able to motivate yourself
• planning, time management and organisational skills
• the ability to deliver under pressure and to tight deadlines
• great attention to detail
• collaborative and teamworking skills to share ideas and develop solutions.

Work experience

Internships are available with large employers, especially in finance, retail and travel. You can also approach smaller companies for placements or shadowing opportunities. Most internships or placements are advertised in the autumn. However, with smaller organisations you may need to make targeted speculative applications to find out about opportunities.

Online data science competitions (e.g. Kaggle, Topcoder) can help you gain visibility and experience. Attending events and conferences also provides valuable networking and learning opportunities.

Speak to your university careers service for local internship and placement advice.

Find out more about the different kinds of work experience and internships that are available.

Employers

The leading employers of data scientists tend to be in the:

• finance
• retail
• ecommerce sectors.

Businesses in these sectors are keen to better understand their audience groups to better target the sales and marketing of their products and offerings.

Other sectors you can work in, include:

• telecoms
• oil and gas
• transport.

You can also work for:

• government departments
• NHS
• universities and research institutes.
• CWJobs
• technojobs

With experience, you could move into client-facing consultancy roles or work for start-ups delivering outsourced data solutions.

Look for vacancies at:

• DataScientistJobs
• CWJobs
• technojobs

Vacancies are also advertised on LinkedIn, and you can stay informed about developments in data science through newsletters such as Data Elixir.

Professional development

After gaining at least two years' experience in a relevant role, you can apply to become an accredited Data Science Professional. You can achieve this by meeting the requirements of a competency framework set out by the Alliance for Data Science Professionals.

Many large employers offer structured graduate schemes with rotations and formal training.

In most companies, training is done on the job, and you'll have the opportunity to learn from experienced colleagues. You'll also be expected to learn and develop new skills yourself by staying up to date with new and emerging technologies and techniques.

Some organisations may provide sector-specific training or support attendance at industry events to expand your knowledge. Large employers (including the Civil Service) have data science graduate training schemes, which typically last around two years.

A postgraduate qualification may also help you specialise further if you don't already have one.

Career prospects

The speed of your progression will depend on how quickly you develop technical skills and your commitment to the organisation.

In most cases, promotion to senior data scientist roles, including team leadership, can be expected within two to five years.

The transferable nature of data science skills makes it easier to switch sectors.

Alternative pathways include joining start-ups working on outsourced projects and transitioning into research roles.