Role Summary
The post holder will be an experienced academic with a strong track record in high-quality teaching, applied research, and academic leadership, with expertise in Data Visualisation & Visual Analytics, Computer Vision & Imaging, Applied Data Engineering at scale, and Generative AI & Large Language Models.
The role combines curriculum leadership, innovative teaching practice, applied research, and knowledge transfer, contributing to the University's strategic priorities in Data Science, AI, and industry-aligned education.
Job Purpose
To lead and deliver excellence in learning and teaching, applied research, and knowledge transfer in Data Science and AI, with a specific focus on:
- Lead the design, delivery, and continuous enhancement of specialist modules in:
- Data Visualisation, Computer Vision & Imaging (30 credits – compulsory)
- Applied Data Engineering and Big Data Analytics
- Generative AI and Large Language Models
- Ensure curricula remain research-informed, industry-relevant, and technologically current, reflecting advances in AI, visual analytics, and cloud computing.
- Deliver high-quality teaching at undergraduate and postgraduate levels using innovative, practice-oriented pedagogies.
- Design and implement hands-on learning activities, including:
- Visual analytics projects using Tableau, Power BI, Python/R visualisation libraries
- Computer vision and imaging workflows
- Cloud-based big data pipelines using EMR, Dataproc, Hadoop, Spark
- Practical LLM fine-tuning, deployment, and integration tasks
- Lead initiatives to enhance student engagement, employability, and learning outcomes, including assessment innovation and authentic industry-linked coursework.
- Provide academic advice, mentoring, and pastoral support to students.
- Supervise undergraduate and postgraduate dissertations aligned to Data Science, AI, visual analytics, and generative AI.
- Work closely with technical, teaching, and administrative staff to maintain a high-quality learning environment.
Research and knowledge transfer
§ Promote and strengthen the research culture and applied research capacity of the Campus in Data Science and AI.
§ Shape, lead, and contribute to applied research projects in areas such as:
oVisual analytics and human-centred data visualisation
oComputer vision and imaging analytics
oScalable data engineering and analytics platforms
oGenerative AI, LLMs, and human-AI interaction
§ Maintain a strong personal research profile, with outputs of recognised international quality.
§ Demonstrate commitment to research impact, including knowledge transfer, industry collaboration, and societal benefit.
§ Attract, supervise, and mentor Masters and Doctoral students.
§ Contribute to securing research, consultancy, and knowledge-exchange funding.
Academic Leadership and Management
- Provide academic leadership in designated areas such as module leadership, programme leadership, or curriculum development, as agreed.
- Contribute to cross-Campus activities, including student recruitment, induction, assessment, partnerships, and external engagement.
- Represent staff and students on Campus and University committees, as required.
- Coach, mentor, and support academic colleagues in teaching innovation and curriculum development.
- Contribute to staff development, performance review, and academic mentoring.
- Manage allocated academic resources efficiently and effectively.
- Undertake other academic or leadership duties aligned with the strategic needs of the Campus.
Requirements
ESSENTIAL REQUIREMENTS
Knowledge, Skills and Experience
- Doctorate (or equivalent) in Data Science, Artificial Intelligence, Computer Science, or a closely related field.
- Strong expertise in Data Visualisation and Visual Analytics, including:
- Principles of visual design, interaction, and evaluation
- Use of tools such as Tableau and Power BI
- Programming-based visualisation using Python and/or R
- Advanced knowledge of Computer Vision and Imaging, including image and video analytics.
- Solid understanding of Applied Data Engineering, including:
- Big data architectures
- Hadoop ecosystem, MapReduce, and distributed processing
- Apache Spark for large-scale analytics
- Hands-on experience with cloud platforms, particularly:
- Google Cloud Platform (Dataproc, GCS)
- Amazon Web Services (EMR, EC2, S3)
- Strong grounding in Generative AI and Large Language Models, including:
- Architectures such as GPT-style and BERT-style models
- Fine-tuning, deployment, and integration of LLMs
- Responsible and ethical use of generative AI
- Proficiency in R, Python, SQL, and familiarity with NoSQL databases.
- Demonstrated ability to deliver high-quality, innovative teaching in Data Science and AI.
- Evidence of high-quality research outputs and active engagement in research projects.
- Proven ability to engage, motivate, and mentor students in a high-performance learning environment.
Experience in supervising postgraduate research students.
DESIRABLE REQUIREMENTS
Knowledge, Skills and Experience
- Industry experience applying data science, AI, or visual analytics to real-world problems.
- Familiarity with analytics applications across sectors such as business, finance, operations, marketing, HR, or public services.
- Track record of securing research, consultancy, or knowledge-exchange funding.
- Experience in doctoral supervision and building research teams.
Postgraduate Certificate in Higher Education (PGCertHE) or equivalent teaching qualification.
