Research Associate (Statistics & Data Science)

Job Title: Research Associate (Statistics & Data Science)

University-Level Unit: Science

Faculty/Department-Level Unit: Statistics and Data Science

Employee Category: Research Staff

Location_ONB: Kent Ridge Campus

Posting Start Date: 19/05/2026

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.