Careers in data

Data professionals 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 (small and medium-sized enterprises) and start-ups, and across all industries, from IT, finance, retail and FMCG (fast-moving consumer goods) to government, research and health.

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

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

Data-related jobs are very varied. From one organisation to another, a data analyst’s role, for instance, can be different in tasks, 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 to talk to someone who's doing the job you're interested in, so you get a real flavour of what it's like (you may wish to use the LSE LinkedIn alumni finder to do this):

• Data analyst – examines large data sets to provide insights and analysis in order to help businesses make strategic decisions. Example tools and skills are Excel, SQL and data visualisation tools such as Power BI and Tableau. Check out the Prospects data analyst job profile.
• Data scientist – turns data into information using algorithms and machine learning. Example tools and skills include Python, R, SQL, and machine learning frameworks like TensorFlow and PyTorch. The Prospects data scientist job profile provides further details. 
• Data engineer – builds and maintains the infrastructure for data collection and processing, and prepares the data for use by data analysts and scientists. Example tools and skills are Python, SQL, Spark and cloud platforms like AWS (Amazon Web Services), Azure or Google Cloud. GOV.UK has a helpful overview of the role and skills required. 
• Prompt engineer – skilled in designing and refining prompts to get the best results from generative AI models. Example tools and skills include a deep understanding of generative AI models (like GPT-5), natural language processing, and strong creative and analytical skills. DataCamp provides a useful guide to the area and the role. 
• Machine learning engineer – enables machines to learn without the need for further programming. Example tools and skills are Python, Java, machine learning libraries like scikit-learn, and experience with model deployment and monitoring. 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. Example tools and skills include Python, R, MATLAB, and expertise in optimisation, simulation and statistical analysis. 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. Example tools and skills include usability testing, survey design, qualitative research methods, and tools like Miro or Figma for user journey mapping. See more on the Prospects UX researcher job profile.

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 with using these professionally? Speaking with LSE graduates via the LSE LinkedIn alumni finder, engaging 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.

Consider how to showcase your development, projects and contributions via an online portfolio – you can present your skills and learning journey by setting up a GitHub profile (which is standard practice in the sector) and a professional website. 

Take advantage of online learning opportunities and certifications, such as those offered via courses on platforms like Coursera, edX or DataCamp. These often provide tangible credentials that can be included on a CV, GitHub or LinkedIn profile. 

If you are early on in your career

Data-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 careers 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. If you’re looking to make a considerable career shift, then targeted upskilling through bootcamps or specialised master's programmes may be a valuable way to develop skills and make connections.

• Technical skills: The most in-demand technical skills mentioned by employers are subject to ongoing change, in keeping with developments in the sector. At present, some of the principal technical skills to explore include top programming languages (such as Python and R), database query languages (primarily, SQL), and specific tools for visualisation (such as, Tableau and Power BI) or machine learning (eg, TensorFlow and PyTorch). Developing experience of cloud platforms (eg, AWS, Azure, Google Cloud) is also important. 
• Soft skills: The ability to work with others is important in the data sector, whether this be immediate colleagues, non-technical staff, clients or other stakeholders. Important skills include the ability to communicate complex findings to non-technical stakeholders (such as through data storytelling) and demonstrating business acumen, which shows employers that you understand how data work contributes to business goals. As a complement to technical skills, the attributes of curiosity, adaptability and a growth mindset are essential in response to the constant changes of the data landscape. 
• Portfolio and practical experience: An interesting portfolio is an essential means of appealing to prospective employers. By presenting a portfolio of projects (eg, on a GitHub profile), you can showcase your ability to apply your skills in a real-world context, which is often more valuable than a list of completed courses. 

LSE alumni are found working in the data sector all over the world. To get a real sense of what a job is like, it's highly recommended to speak with someone already in the role or area of interest (you may wish to use the LSE LinkedIn alumni finder to do this). 

Many alumni and employers participate in events like the LSE Careers Discover programmes (such as Discover | Data and Discover | Tech), which feature alumni panels and skills seminars with opportunities to experience hands-on introductions to data and AI. These events offer valuable networking opportunities with alumni and employers. 

When you're preparing for a career in data, it's crucial to understand what employers are really looking for beyond your academic qualifications. This section will give you an inside look at how to stand out and successfully navigate the recruitment process. 

What employers look for on your CV

Employers want to see evidence that you can apply your skills in a practical way. Highlight projects you've worked on, whether from your coursework, an internship or a personal side project. Make sure to list specific technical skills like programming languages (Python, R, SQL) and data visualisation tools (Tableau, Power BI) clearly. Where possible, quantify your achievements to demonstrate your impact – for example, "analysed a data set of 10,000 records and identified key findings" or "improved a process by X per cent". 

The interview process 

Data-related interviews often have multiple stages that are designed to assess both your technical abilities and soft skills. Be prepared for: 

• Behavioural questions: These are used to evaluate your teamwork, communication and problem-solving skills. 
• Technical assessments: You may be given a coding challenge, a case study or a take-home assignment to test your practical skills. 
• Data-specific questions: You could be asked about your approach to a data problem or your understanding of key concepts. 

The power of a portfolio 

For many data roles, a portfolio of projects is just as important as your CV. Platforms like GitHub are excellent for showcasing your projects, code and your problem-solving process. This is especially valuable if you don't have a traditional tech or coding degree, as it provides tangible proof of your abilities. 

In-house and online training and development 

Don't worry if you feel you're lacking a formal tech background. Many organisations are willing to invest in new hires and provide comprehensive training. For technical roles, employers expect you to be willing to learn quickly and participate in in-house training and development programmes (which may encompass completing online courses and certifications). This is a common entry point for graduates from all academic disciplines into data and IT roles. 

The data sector is one of the most rapidly evolving fields, so staying current with emerging trends is crucial for your career. Here’s a look at some of the key trends shaping the industry and where you might see future opportunities. 

Generative AI and large language models (LLMs) 

Generative AI is one of the most significant trends right now. These models, like GPT-5, are transforming how businesses operate by automating content creation, code generation and customer service. This has led to the emergence of specialised roles like prompt engineer, and it's making foundational knowledge of large language models a highly sought-after skill. 

The rise of responsible AI 

As AI becomes more integrated into our daily lives, there's a growing focus on AI ethics and responsible practices. This trend is driven by the need to ensure that AI systems are fair, transparent and secure. Professionals in this area work to mitigate algorithmic bias and protect data privacy, making it a critical and growing field for those interested in the social and ethical implications of technology. 

Cloud-native data platforms 

Many organisations are shifting their data infrastructure to cloud platforms like AWS, Azure and Google Cloud. This allows for greater scalability and flexibility in how data is stored, processed and analysed. As a result, skills in cloud computing and specific big data tools like Spark and Hadoop are becoming essential for data engineers and scientists. 

Data-driven decision making across all industries 

The use of data is no longer limited to the tech and finance sectors. Businesses in almost every industry – from retail to healthcare to government – are using data analytics to improve operations and inform strategy. This means that data skills are in demand across a wide variety of sectors, opening up new career paths for graduates with strong quantitative and analytical skills. 

• 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 and LSE Careers organise a yearly Python coding challenge open to all LSE students, as well as a social impact hackathon over the summer break. These are good ways 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 is organising a range of events, as is the LSE Students' 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 AI, 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.
• Check out this useful list of Hackathons in London on Eventbrite.

LSE Careers resources:

• Blog: Resources to prepare for a career in Data and Tech
• Blog: From LSE to a career in digital health
• Blog: Cracking into data science as a social scientist
• Blog: Considering a career in data science
• CareerHub: Materials and recordings related to data
  

Online courses to develop your technical skills:

• The LSE Digital Skills Lab is a great place to develop your technical and coding skills and to find a list of resources, online courses and workshops. On their website you can find the graduate digital skills in demand, in different employment sectors.

Other online course platforms include:

• Codecademy
• Udemy
• Udacity
• Hack Design
• Coursera 
• edX
• DataCamp. 

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.

• Exponential View, a podcast by Azeem Azar
• Towards Data Science
• Springboard
• Dataquest’s blog offer tutorials for learning data science techniques and technologies
• Kaggle gathers a community interested in AI, machine learning and data science, offers free courses, shares public datasets, organises competitions and has a blog
• for blogs, news tutorials on R and Python, you can go to R Weekly,  R-bloggers and Py Coder's Weekly.

Resources to prepare for technical interviews: 

• HackerRank has 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 has listed the best coding challenge websites, which are a great way to prepare for coding interviews.
• Interview Cake offers 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 students and graduates.

Professional bodies and associations:

• The British Computer Society
• The Institute of Analytics
• The Royal Statistical Society
• The European Association for Artificial Intelligence
• The Society for the Study of Artificial Intelligence and Simulation of Behaviour

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

• Data Scientist Jobs
• Data Jobs
• eFinancialCareers jobs in tech
• Indeed IT graduate jobs
• IT Graduate Jobs
• Milkround data graduate jobs
• Only Data Jobs
• Silicon Milkroundabout

For jobs specifically in start-ups:

• Wellfound (formerly known as AngelList Talent)
• Work in Startups
• We Work Remotely 
• Welcome to the Jungle 
• Jumpstart is a start-up graduate programme for non-tech roles. You can watch their presentation at LSE here.

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.