Assistant Professor (Education) in Data Science

Association for Computing Machinery (MY)
City of London
4 months ago
Applications closed

Related Jobs

View all jobs

University Assistant Professor in Machine Learning

Lecturer in Machine Learning for Engineering

Lecturer in Machine Learning for Engineering

Assistant and Associate Professor positions in Statistics and Machine Learning at Warwick

Faculty in Data Sciences - Critical Infrastructure and Data Transformation (CID) to Advance National Security

Data Scientist Assistant

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.

Department of Statistics

Assistant Professor (Education) in Data Science

Salary is no less than £68,087 per annum and the salary scale can be found on the LSE website.

Applications are invited for this post from outstanding teachers in the field of data science, with a focus on computational aspects. The successful candidate will join a vibrant research and teaching environment in the Department of Statistics. Data science is a key priority area in the LSE 2030 strategy, offering exciting opportunities to create new initiatives, foster collaborations, and make a significant impact in this field.The postholder will contribute to the teaching and management of the MSc Data Science, the new BSc Economics and Data Science, and courses developed for other departments. The post is tenable from 1st September 2026.

Please note that this is an Education Career Track post. Candidates for these posts should have a proven track record of excellence in teaching and a strong commitment to education.

Candidates should have a strong track record in teaching; the ability to teach computer science courses on topics such as programming, databases, and distributed computation for processing large datasets and solving large-scale machine learning tasks at undergraduate and postgraduate level; experience in teaching that involves the use of modern data science software tools and technologies; experience or interest in usingreal-world datasets in teaching; and strong interpersonal and networking skills.

The other criteria that will be used when shortlisting for this post can be found on the person specification, which is attached to this vacancy on the LSE’s online recruitment system.

In addition to a competitive salary the benefits that come with this job include occupational pension scheme, a collegial environment and excellent support, training and development opportunities.

For further information about the post, please refer to the ‘How to Apply’ document, job description and the person specification.

To apply for this post, please go to www.jobs.lse.ac.uk. If you have any technical queries with applying on the online system, please use the “contact us” links at the bottom of the LSE Jobs page. Should you have any queries about the role, please email

The closing date for receipt of applications is 14 December 2025 (23.59 UK time).

We are unable to accept any late applications.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.