Lecturer (Teaching and Scholarship) in Music and Data Science

University of Leeds
Leeds
2 months ago
Applications closed

Related Jobs

View all jobs

Lecturer in Computing & Data Science (Evenings/Weekends)

Lecturer in Statistics & Data Science — Research & Teaching

Lecturer in Data Engineering

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

Lecturer in Data Science & AI (Practice‑Focused)

Data Engineering Lecturer — AI, Cloud & Research

Lecturer (Teaching and Scholarship) in Music and Data Science

University of Leeds


Are you an enthusiastic teacher, practitioner or researcher, committed to delivering a first-class learning and teaching experience with a demonstrated ability to teach effectively at undergraduate and postgraduate level? Are you passionate about delivering an exceptional student experience in a research-intensive Russell Group University?


The School of Music seeks to appoint a Lecturer in Music and Data Science to lead and develop our new Masters programme in this area, and to enhance educational provision focusing on data science applications in the music industry. You will offer demonstrable experience across both fields, whether acquired through industry experience, formal training, or active practice, and will bring together technical expertise, cutting‑edge understanding of contemporary applications of data science, and a genuine interest in music education. We particularly encourage applicants with combined experience in data science and digital marketing within the global music industry, whether in recording/streaming, music publishing or live music contexts.


Position Details

  • Mid/Senior level
  • Part‑time
  • Education and Training
  • Higher Education

Responsibilities

  • Teach undergraduate and postgraduate students in your specialist field using a diverse range of methodologies.
  • Carry out teaching, scholarship and management within the School.
  • Contribute to the supervision of student projects and the delivery of our curriculum beyond your specialist area.
  • Lead and develop our new Masters programme in Music and Data Science.

Qualifications

  • Postgraduate degree or relevant professional experience in music, data science, or related fields.
  • Demonstrated ability to teach effectively at undergraduate and postgraduate level.
  • Experience that combines technical expertise in data science with a genuine interest in music education.
  • Experience with data science and digital marketing in the global music industry is highly desirable.

Benefits

  • 26 days holiday plus approximately 16 Bank Holidays/University‑closed days – 42 days a year!
  • Generous pension scheme options plus life assurance.
  • Health and wellbeing: discounted staff membership to The Edge campus gym, pool, sauna, climbing wall, cycle circuit and sports halls.
  • Personal development: access to courses run by our Organisational Development & Professional Learning team.
  • On‑site childcare, shopping discounts and travel schemes.

Contact

Professor Bryan White, Head of School
Email:


#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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.