Assistant Professor in Actuarial Data Science (T&R)

Heriot-Watt University
Kilmarnock
1 month ago
Applications closed

Related Jobs

View all jobs

University Assistant Professor in Machine Learning

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

Lecturer in Machine Learning for Engineering

Lecturer in Machine Learning for Engineering

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

Data Scientist Assistant

Assistant Professor in Actuarial Data Science (T&R)

Directorate: School of Mathematical and Computer Sciences


Salary: Grade 7 – £37,694 – £47,389 / Grade 8 – £47,389 – £58,225


Contract type: Full Time (1FTE), Open Ended


Rewards and Benefits: 33 days annual leave, plus 9 buildings closed days for all full time staff (Part time workers pro rata by FTE). Use our total rewards calculator: https://www.hw.ac.uk/about/work/total-rewards-calculator.htm.


Seniority level: Mid‑Senior level


Job function: Education and Training


Industries: Higher Education


Detailed Description

The Department of Actuarial Mathematics and Statistics at Heriot‑Watt University, Edinburgh, seeks to enhance its research and teaching in actuarial science and statistics by appointing an Assistant Professor in Actuarial Data Science, or a related actuarial statistics area. Applicants from statistical learning, actuarial statistics or related areas are encouraged. Candidates interested in the university’s multi‑disciplinary Global Research Institutes in climate change & sustainability or healthcare are especially welcome.


Key Duties and Responsibilities

  • Lead, carry out and publish internationally excellent research in actuarial data science, actuarial statistics or a related field.
  • Apply for research funding via grant proposals or industry funding to build a research group.
  • Undertake knowledge exchange activities to promote and disseminate research.
  • Perform administrative and recruitment activities as required.
  • Develop and deliver innovative teaching at undergraduate and postgraduate level.
  • Report to the Head of Department, maintaining and enhancing the School’s reputation for excellence.

Education, Qualifications and Experience

Essential criteria:



  • E1. PhD in actuarial science, statistics, or a related field.
  • E2. Track record of high-quality research in actuarial data science with internationally excellent publications.
  • E3. Demonstrable teaching experience and skills to supervise undergraduate and postgraduate dissertations.
  • E4. Excellent interpersonal and teamwork skills.
  • E5. Potential, ambition and plans to obtain research funding.
  • E6. Ability to supervise PhD students successfully.

Desirable criteria:



  • D1. Track record of obtaining research funding.
  • D2. Successful supervision of PhD students and/or post‑doctoral researchers.
  • D3. Potential to lead research strategy and develop learning and teaching activities.

How to Apply

Submit via the Heriot‑Watt University online recruitment system:



  1. Cover letter describing interest and suitability.
  2. Full CV, including publication list.
  3. Outline of research plans for next few years.
  4. One-page summary of teaching philosophy or approach.

Applications accepted until midnight on Sunday 18th January 2026.


Contact

For questions, contact Head of Department, Professor George Streftaris – .


Equality, Diversity and Inclusion

Heriot‑Watt University is committed to securing equality of opportunity in employment and creates an environment of merit-based selection, training, promotion and treatment. Diversity and inclusion are central to our culture. For more information, see https://www.hw.ac.uk/uk/services/equality-diversity.htmand also our Disability Inclusive Science Careers at https://disc.hw.ac.uk/.


#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.