The best data scientists enjoy problem-solving, have strong analytical and statistical skill sets and are confident communicators
Organisations are increasingly using and collecting larger amounts of data during their everyday operations. Data scientists turn data into information using algorithms and machine learning. They take on projects to meet a particular customer or business need and present their results using clear and engaging language.
Data scientists are in high demand across a number of sectors as businesses require people with the right IT skills. You'll be curious and enthusiastic about data, but have the necessary communication skills to explain your findings to non-experts.
Types of data scientist
You can work across a broad range of areas, including:
- scientific research
- information technology
As a data scientist you'll need to:
- work closely with your business to identify issues and use data to propose solutions for effective decision making
- merge, manage, interrogate and extract data to supply tailored reports to colleagues, customers or the wider organisation
- maintain clear and coherent communication, both verbal and written, to understand data needs and report results
- use machine learning tools and statistical techniques to produce solutions to problems
- create clear reports that tell compelling stories about how customers or clients work with the business
- horizon scan to stay up to date with latest technology, techniques and methods
- conduct research from which you'll develop prototypes and proof of concepts
- look for opportunities to use insights/datasets/code/models across other functions in the organisation (for example in the HR and marketing departments)
- stay curious and enthusiastic about using algorithms to solve problems and enthuse others to see the benefit of your work.
- Entry level salaries can range from £19,000 to £25,000.
- With a few years' experience you could expect to earn £30,000 to £50,000.
- Experienced, high-level data scientists or contractors can earn upwards of £60,000, in some cases reaching more than £100,000.
Benefits vary depending on the organisation but are likely to include a company pension scheme, flexible or remote working, performance bonuses and private medical insurance.
Figures are intended as a guide only.
Depending on the type of company you work for you can expect a good work/life balance. Core office hours can be anywhere between 8am - 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 area, with many people sharing their methodologies and insights, so you should be prepared to share your ideas and solutions with your wider team.
- You can probably expect to wear casual office attire. Jeans and jumpers are preferred over business suits in most data scientist roles. This may vary across companies, so doing your research before you join is important.
- 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.
- Data science is currently a male-dominated field, although initiatives such as Women in Tech are working to redress the imbalance.
A degree will often be required, but it does not necessarily have to be in a computer or science based area. Having strong quantitative skills is a good base, but an interest in data and being able to solve problems logically and methodically are often bigger factors.
You'll be expected to know some programming languages such as R or Python and have strong database design and coding skills.
Postgraduate degrees in data science are becoming more popular, but they are not usually required. If you're considering a change of career, or are interested in learning analysis skills, studying for a postgraduate qualification may be worthwhile. They're offered in subjects such as:
- MSc Data Science
- MSc Business Analytics
- MSc Data Science and Analytics
- MSc Big Data.
You'll need to have:
- exceptional communication skills, in order to explain your work to people who don't understand the mechanics behind data analysis
- great attention to detail and the ability to problem solve
- experience in (or a willingness to get to grips with) database interrogation and analysis tools, such as Hadoop, SQL and SAS
- 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
- good planning and organisational skills
- a collaborative approach to sharing ideas and finding solutions.
Internships are available in data science at a number of the bigger employers, particularly in finance, retail and travel. You could also approach smaller to medium size enterprises for internship or shadowing opportunities. Most internships or placements are advertised in the autumn, however with smaller organisations you might need to make speculative applications discover their opportunities.
Most organisations like their entry-level data scientists to have gained some work experience prior to applying for a job, but undertaking self-directed learning in programming or analysis during your course will demonstrate your enthusiasm for the role.
Talk with your university careers service for advice on where to search for internships and placements locally.
The leading employers for data scientists tend to be in the finance, retail and e-commerce sectors. Businesses in these sectors seek to better understand their audience groups, in order to target their focus on relevant products and offerings.
Sectors such as telecoms, oil and gas and transport are increasingly using big data to make decisions that could positively impact their workforce, operations or sales.
Online data science competitions, such as those hosted by Kaggle, Topcoder or the Defence Science Technology Laboratory's (DSTL) new data science challenge, are also used by employers to spot new and emerging talent.
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While there's no official accreditation for working in data science, you'll build on your analytical and technical skills once you start to gain experience. You'll be expected to learn how the wider company operates to ensure you're identifying business problems that need to be solved.
Some companies may offer additional training in their operating procedures or encourage you to attend sector specific events to help your understanding of potential issues, new developments or emerging trends. This knowledge can then be used to better apply your problem-solving skills to relevant projects.
Progress will depend on your ability to quickly learn the relevant skills needed to analyse large data sets, as well as your commitment to the organisation you are working for. You can be promoted to more senior data science roles within your company which may involve line management of junior data scientists.
Promotion from junior to senior data scientist can take between two to five years. After five years you'll be expected to take on more people management responsibility.
The skills you acquire are transferable across a range of sectors so it can be relatively easy to move into different companies.
An alternative pathway is to join a start-up company and work on projects outsourced by larger organisations.