At a Glance
- Tasks: Conduct groundbreaking research and teach innovative computational social science courses.
- Company: LSE, a leading institution committed to diversity and inclusion.
- Benefits: Competitive salary, pension scheme, generous research leave, and professional development opportunities.
- Other info: Collaborative environment with excellent career growth and support.
- Why this job: Join a vibrant academic community and make an impact in social science research.
- Qualifications: PhD or nearing completion, with expertise in computational and quantitative methods.
The predicted salary is between 68087 - 68087 £ per year.
LSE is committed to building a diverse, equitable and truly inclusive university. As an equal opportunities employer strongly committed to diversity and inclusion, we encourage applications from women and those of Minority Ethnic backgrounds as they are currently under-represented at this level in this area. All appointments will be made on merit or skill and experience relative to the role.
The Department of Methodology is a leading centre for research and education in social science research methods. The Department has a thriving research culture, with its members advancing research in core social science disciplines as well as in research methodology. It leads the provision of research methods education to graduate students across the range of disciplines at LSE.
The Department is recruiting an Assistant Professor in Computational Social Science with expertise in quantitative methods. Based in the Department of Methodology, the post holder will contribute to the intellectual life of the School through conducting and publishing outstanding research, engaging in high quality teaching as instructed by the Head of Department, and participating in the School and wider Department activities.
Applicants should have completed a PhD, or be close to obtaining a PhD, and have research expertise in applying computational and quantitative methods to an empirical social science discipline or disciplines. They should have a track record or trajectory of internationally excellent publications and the ability to teach a range of computational social data science courses. This includes the application of computational methods to social scientific research problems, programming, data wrangling and analysis. We are especially interested in candidates who use generative AI to address methodological and substantive research questions and who can develop new and contribute to existing courses in this area.
In addition to a competitive salary, the benefits that come with this job include an occupational pension scheme, a research incentive scheme with personal reward options, generous research leave (sabbatical) entitlement, a collegial faculty environment and excellent support, training and development opportunities.
The closing date for receipt of applications is 11th October 2026 (23.59 UK time). We are unable to accept any late applications.
Assistant Professor in Computational Social Science employer: The London School of Economics and Political Science (LSE)
The London School of Economics and Political Science (LSE) is an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. With competitive salaries and the flexibility of hybrid working conditions, LSE prioritises employee growth through continuous learning opportunities and professional development. Working at LSE not only provides a chance to contribute to a prestigious institution but also to be part of a community dedicated to shaping future leaders in economics and social sciences.
Contact Details:
The London School of Economics and Political Science (LSE) Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Assistant Professor in Computational Social Science
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like The London School of Economics and Political Science (LSE)!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Assistant Professor in Computational Social Science at The London School of Economics and Political Science (LSE).
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like The London School of Economics and Political Science (LSE).
✨Apply Directly through Our Website
When you find a suitable opening like Assistant Professor in Computational Social Science at The London School of Economics and Political Science (LSE), make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Assistant Professor in Computational Social Science
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at The London School of Economics and Political Science (LSE), your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at The London School of Economics and Political Science (LSE). Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at The London School of Economics and Political Science (LSE)
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at The London School of Economics and Political Science (LSE)!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
