Careers in data

This guide seeks to provide you with an overview of what it would mean to work in Data, ie, in professional areas such as data analytics, data science, artificial intelligence and machine learning. You will find here an overview of the types of roles, types of organisations offering opportunities and how you can get started, including links to employers, jobs boards and further sources of information. If you are interested in exploring careers in the technology sector and its related employers and roles, you can also read our Careers in tech employment sector page.

What is a career in data?

The data sector is extremely dynamic, quickly evolving, and impacting and shaping many aspects of the world we live in. Large datasets have become available in the last decade, enabling the development and growth of a wide range of uses and applications, for example in IoT (Internet of Things), speech and face recognition, or autonomous vehicles.

  • Data analytics examine large data sets to understand them, identify trends, and provide insights and analysis with visuals such as charts, and through strong written and verbal communication skills, to help businesses make strategic decisions.
  • Data science is a science in the sense that it digs out data from an uncertain and unstructured content, not being sure about what will be found out, in order to make sense of it. In other words, its aim is to extract knowledge and insights from structured and unstructured data, using analytics, algorithms, machine learning and statistical tools, and building predictive statistical models. A Data scientist combines technical, analytical and communication skills.
  • AI is the broad science of creating intelligent machines that can simulate human thinking capability and behaviour, such as learning and problem-solving.
  • Machine learning is a branch of artificial intelligence to create programmes and algorithms that enable computer systems continue learning and improving on their own, based on data and experience, pattern identifications, and to take actions without direct instruction. It utilises data to improve performance or inform predictions.

The data sector provides a range of dynamic and innovative working environments and the opportunity to develop sought-after skills and knowledge. It is a real asset to have some technical skills and a genuine interest in technology. However, employers also recruit graduates with no prior technology or coding skills or degree.

For technical roles, you would need to prove you are willing to learn quickly and take part in in-house training and development. More and more organisations are accepting applications from graduates across all academic disciplines for roles within data and IT, opening up new career paths for LSE students with a passion for technology and analytics.

The growing trend for both private and public sector organisations to apply data analytics to business decision making is also a key influencer on graduate labour market trends. With the adoption of new technologies, such as machine learning, deep learning and natural language processing, highly analytical graduates with strong quantitative skills, are actively sought by employers. Transferable skills such as problem solving, communication and teamwork, as well as the eagerness to learn, are critical and will be assessed in the recruitment process. 

Where can I work, what can I do, and how can I get there?

Where can I work?

Data analysts and data scientists can work in most types of organisations, from big corporate organisations (including the ‘tech giants’ Alphabet, Amazon, Apple, Meta and Microsoft) to the public sector, to consultancies (such as the ‘Big Four’ (Deloitte, EY, KPMG, PwC), or Accenture), to SMEs and start-ups, and across all industries, from IT, finance, retail and FMCG to government, research and health.

Here are a few examples of graduate programmes in data analytics or data science that are open to recruit graduates from a wide range of different degree: Accenture, MI5, PwC, NHS, Lloyds Banking Group and the Data Science Campus (within the Office for National Statistics).

The Careers in Tech page lists a range of tech focused employers, amongst the main employers of roles in data, but beyond the tech sector, these are in increasing demand in a wide variety of sectors and organisations.

What can I do?

Data related jobs are very varied. From one organisation to another, a data analyst’s role can be different in tasks and responsibilities, and the level of technical skills involved. It’s a good idea to go beyond job titles and read job descriptions and person specifications carefully.

We've outlined some of the roles likely to be of most interest to LSE students, with links to where you can find more information. If you can, try and talk to someone who's doing the job you're interested in, so you get a real flavour of what it's like.

  • Data analyst – examines large data sets to provide insights and analysis in order to help businesses make strategic decisions. Check out the Prospects Data analyst job profile.
  • Data scientist – turns data into information using algorithms and machine learning. The Prospects Data scientist job profile provides further details. 
  • Machine learning engineer – enables machines to learn without the need for further programming. More information can be found on the Prospects Machine learning engineer job profile.
  • Operational researcher – uses both mathematical and computational modelling to support decision making and strategy within a range of organisations. Check out the Prospects Operational researcher job profile.
  • UX researcher – delivers the best possible experience for the users of a website or application, making its use as straightforward as possible, by researching and analysing user behaviour and preference and considering design elements such as colours and images. See more on the Prospects UX researcher job profile.

How can I get there?

An honest appraisal of your current technical skills, and your willingness and capacity to learn and develop in this area is a good starting point. For example, if you have developed programming and coding skills, how confident do you feel in this area? Speaking with employers on campus and attending events run by LSE Generate and LSE Careers can help clarify which role might suit you.

If you are looking to gain experience

Internships and work experience will provide an advantage but are not the only option. Look out for coding clubs, hackathons and other collaborative data focused events on campus or beyond. Meeting with students from different institutions, including those with technical and engineering degree programmes such as UCL and Imperial, can help develop your knowledge and skills along with your network of contacts.

If you are early on in your career

Data analytics-related graduate programmes are available across a range of organisations. A graduate programme can be a good way of accessing training and development opportunities for non-IT graduates, and are often used as an entry point into this field by LSE students. Alternatively, you may wish to explore opportunities within SMEs and start-ups where your drive, adaptability and entrepreneurial skills will be equally valued.

If you have more experience

The best route is likely to be through recruitment agencies and search firms, networking within the sector and applying directly for roles on company websites and via LinkedIn. An ability to demonstrate your continuous professional development in relation to data, coding and innovation will be key to ensuring that your previous work experience is relevant to future employers.

If you’re changing career

Reflect on what your existing skills might add to a data-focused role. Be prepared to be flexible. LSE Alumni who have changed career often emphasise the important of making a number of moves in the first few years. This blog from LSE Careers highlights some of the areas to focus on. 

Where can I find out more about working in this sector?

Interested in finding out more about a career in data?

Here are some helpful links, including the ways LSE Careers can work with you on your journey.


  • Look out for events and resources part of the Discover | Data programme. In particular, employer skills seminar on data analytics and data science are a good way to hear from professionals how they use data in specific case studies.
  • The LSE Digital Skills Lab with LSE Careers are organising a yearly Python Coding Challenge open to all LSE students. This is a good way to gain experience in data and meet with likeminded students in a team. 
  • To engage with the start-up and innovation world at LSE, Generate offers a lot of networking opportunities and events with entrepreneurs. 
  • The LSE Data Science Institute are organising a range of events. So is the LSE Student Union Data Science society
  • Bear in mind that London is one of the biggest tech hubs in the world, and there are a lot of London meet-up groups in the data sector. On Meetup, you can find a list of groups dedicated to different areas of data analytics and data science fields and identify events you’d like to attend. You can also choose locations other than London or the UK. 
  • There is a range of other events in London, in the UK and overseas in the data sphere, listed by TechMeetups, Silicon Milkroundabout or Tech.London.
  • Here is also a useful list of Hackathons in London.


LSE Careers resources:

Online courses to develop your technical skills:

Other online courses include:

External resources:

There is a large number of resources you can access to increase your knowledge about data science, data analytics, programming languages, machine learning and artificial intelligence, and keep up to date with future trends.

Resources to prepare for technical interviews: 

  • HackerRank have a platform with coding tutorials and practice problems, along with interview preparation challenges and tips.
  • Codecademy gives access to help with preparing technical interviews.
  • Coderbyte have listed the best coding challenge websites, which are a great way to prepare for coding interviews.
  • Interview Cake offer advice on technical interviews and list programming interview questions and how to solve them.

Active tech communities: 

  • LeetCode is a platform to help you enhance your skills, expand your knowledge and prepare for technical interviews. 
  • Blind is an anonymous professional network.
  • Women in Tech has resources and advice for female graduates.

Professional bodies and associations:

Jobs and opportunities

Job boards are a good way to understand what roles are out there in data analytics or data science in any sector, and what are the technical as well as soft skills required.

For jobs specifically in start-ups:


If you’d like to discuss your options in this sector, or chat through your current plans, please book an appointment with an LSE Careers Consultant.