Research Assistant (Statistics & Data Science)
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:
- Bachelor’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.
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