Assistant Professor (Education) in Data Science

London School of Economics and Political Science
City of London
3 months ago
Applications closed

Related Jobs

View all jobs

Purchasing Data Quality Support Assistant

Purchasing Data Quality Support Assistant

Data Analyst Senior Consultant, Assistant Manager, Manager - Belfast

Data Analyst Training Course (Excel, SQL & Power BI)

Trainee Data Analyst - Training Course

Trainee Data Analyst - Training Course

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 using real-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 Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.