Lecturer in Mathematics (Data Science)

University of Greenwich
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Lecturer in Accounting & Data Analytics

Programming & Data Science Private Tutor

Lecturer in Computing HE Data Science and AI

Lecturer in Computing (HE) (Data Science and AI)

Lecturer in Computing (HE) (Data Science and AI)

Lecturer in Music & Data Science — Lead MSc Program

The School of Computing and Mathematical Sciences at the University of Greenwich is seeking to recruit two new Lecturers in Mathematics, with Teaching, Research and Knowledge Exchange expertise in their field and solid experience in Data Science applications. This is an opportunity to join a vibrant teaching and research environment, with over 120 staff and PhD students working on current and future challenges in Computer Science, AI, Cyber Security and Mathematical Sciences. 

We expect the candidates to have a background that will complement the existing research activities related to their field of expertise. These posts will be expected to be allocated to the one of the University’s Career Pathways, involving participation and leadership in Teaching, and strong emphasis on Research and Enterprise activity.

The successful candidates will work closely with academic teams in the School of Computing & Mathematical Sciences and be expected to contribute to existing Teaching, Research and Enterprise. You will be able to demonstrate a strong research profile, teaching expertise, and student supervision experience at undergraduate and postgraduate level. You are required to hold a PhD in Computer Science, Engineering, or a closely related field.

Experience delivering systems according to GDPR compliance would be desirable.

You must demonstrate eagerness to collaborate with future colleagues within the School and the University. Experience in multi-disciplinary research, track record of participation in Research and Knowledge Exchange bids, and experience in developing new undergraduate or postgraduate modules are highly desirable. 

The school offers ambitious and highly motivated individuals the opportunity to develop both their research and teaching profiles within a positive and innovative academic environment.

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.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.