Assistant Professor in Statistical Data Science

Heriot-Watt University
Kilmarnock
3 weeks ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist

Senior Data Scientist

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

Data Science Faculty of Data-Driven AI in Special Education (Tenure Track/Tenured, F1051A)

Research Assistant/Associate in Data Science and Computational Neuroscience

Research Assistant in Machine Learning for Clinical Trials

Assistant Professor in Statistical Data Science

Join to apply for the Assistant Professor in Statistical Data Science role at Heriot-Watt University.

Overview

The Department of Actuarial Mathematics and Statistics at Heriot-Watt University, Edinburgh, is seeking to enhance and expand its strengths in research and teaching by appointing an Assistant Professor in Statistical Data Science, or related area. This is an open-ended position.

Role and Responsibilities
  • Lead, carry out and publish internationally excellent research in statistical data science, or a related field.
  • Apply for research funding through either the submission of high-quality grant proposals or funding from industry, with the goal of building a research group.
  • Undertake knowledge exchange activities to promote and disseminate research.
  • Carry out administrative and recruitment activities as required to achieve these aims.
  • Develop and deliver innovative teaching in statistics, actuarial science, financial mathematics or related fields at undergraduate and postgraduate level.
  • Teach on the Data Science joint programme with Xidian University.
  • Be responsible to the Head of Department for performing the above activities in a way that maintains and enhances the School’s reputation for excellence.
Education, Qualifications and Experience

As a successful candidate, you will lead, carry out and publish internationally excellent research in your field. You will have a strong track record of research in actuarial data science, which may also include machine learning, financial risk and climate change risk, demonstrated through publications, citations, external invitations and research funding.

You will be established as an international research leader, with the ambition to build a world-class academic group and have the experience or potential to supervise PhD students and post-doctoral researchers. You will have the relevant experience to engage in and innovate our specialised statistical, data science, actuarial and financial degree programmes. You will have the drive and commitment to contribute to the expansion of our teaching programmes.

Essential Criteria
  • E1. PhD in statistics, or related field.
  • E2. Track-record of high-quality research in statistical data science with internationally excellent publications.
  • E3. Demonstrable teaching experience related to the Department’s courses, as well as skills to supervise undergraduate and postgraduate dissertations in Statistical Data Science.
  • E4. Excellent interpersonal and teamwork skills.
Desirable Criteria
  • D1. Track record of obtaining research funding.
  • D2. Track record of successful supervision of PhD students and/or post-doctoral researchers.
  • D3. Potential to provide leadership in the development and implementation of research strategy and in the planning, organisation and development of learning and teaching activities in the Department.
How to Apply

Interested applicants must submit via the Heriot-Watt University online recruitment system: (1) a cover letter describing interest and suitability for the post; (2) a full CV, including a list of publications; (3) an outline of research plans for the next few years; and (4) a one-page summary of teaching philosophy or approach.

Applications can be submitted until midnight on Monday 2 February 2026. Shortlisting is expected in the week of 9 February, with interviews in late February or early March.

Contact

If you have questions, you may contact the Head of Department, Professor George Streftaris ().

About the Institution

Heriot-Watt University values diversity and equality of opportunity in employment and aims to create an inclusive environment. The university is committed to equality and diversity and welcomes applications from all sectors of society.


#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.