Job Description

The successful candidate will contribute to the development and delivery of practice-oriented teaching and applied data science initiatives within the department. This role combines hands-on technical support for modern data science environments with opportunities to participate in student and industry projects, enabling the translation of best practices into real-world applications.

The main responsibilities of the position include:

• Supporting the development and maintenance of teaching platforms and environments used in data science and AI courses.
• Managing code, data, and model lifecycle workflows (e.g., version control, data versioning, experiment tracking, and model management) to support teaching and project work.
• Designing and implementing reproducible, scalable, and well-documented data science pipelines for instructional and applied use.
• Conducting and supporting practical lab sessions on data science tools, workflows, and best practices.
• Supporting the management and utilisation of computational resources (e.g., GPU servers and related infrastructure).
• Contributing to student and industry project work, including data preparation, modelling, evaluation, and deployment-related tasks where relevant.
• Collaborating with faculty and project teams to translate industry practices into teaching materials and applied workflows.
• Keeping abreast of current industry practices, tools, and standards in data science, AI, MLOps, and DevOps, and contributing to their adoption in both teaching and project settings.

Qualifications

Qualifications / Discipline:

• Master’s degree in Data Science, Computer Science, Information Systems, or a related field

Skills:

• Proficiency in Python and common data science libraries.
• Familiarity with version control systems (e.g., Git).
• Exposure to data and model lifecycle tools (e.g., data versioning, experiment tracking, model management).
• Understanding of end-to-end data science workflows, including data preparation, modelling, evaluation, and deployment concepts.
• Basic experience with containerisation and environment management tools (e.g., Docker).
• Ability to work with computational environments (e.g., Linux-based systems, cloud or on-premise infrastructure).
• Strong problem-solving, organisational, and documentation skills, with attention to reproducibility.
• Good communication skills and ability to work with both technical and non-technical stakeholders.

Experience:

• Experience working on data science or machine learning projects (academic or industry).
• Exposure to collaborative project environments (e.g., team-based development, shared repositories).
• Prior experience supporting teaching, conducting labs, or mentoring students is advantageous.
• Experience with modern MLOps and/or DevOps practices (e.g., CI/CD, containerised workflows) is highly desirable.
• Experience or interest in applied projects (e.g., industry collaborations, consulting, or capstone projects) is a plus.