Lecturer, Data Literacy & AI Pedagogy

National University of Singapore; Singapore; On-site; SGD 84,000 - 120,000; Full time

Job summary

National University of Singapore invites applications for a Lecturer (Data Literacy) in the General Education Unit hosted by the Office of the Provost. The appointment is full-time, renewable under the NUS Educator Track, with collaboration across Mathematics and Statistics & Data Science.

The role focuses on curriculum design for GEA1000 Quantitative Reasoning with Data, integrating AI into teaching, and aligning with higher-level data science courses.

Qualifications

• PhD in statistics, mathematics, physics or a closely related quantitative discipline is required.
• Experience delivering university-level general education courses.
• Proven ability to integrate AI and educational technologies into teaching.
• Strong content design, assessment development and program alignment experience.

Responsibilities

• Curriculum Design & Delivery: refine content, delivery and assessment for GEA1000.
• Educational Innovation: integrate AI and technologies into teaching pedagogies.
• Curriculum Alignment: collaborate with units to align with higher-level data courses.

Skills

Data science

Analytics

Excel

Python

R

Higher education teaching

Curriculum development

Pedagogical innovation

Scholarship of Teaching & Learning

Education

PhD in Statistics, Mathematics, Physics, or closely related quantitative discipline

Job description

Job Title: Lecturer (Data Literacy)

University-Level Unit: Office of the Provost

Faculty/Department-Level Unit: General Education Unit

Employee Category: Other Teaching Staff

Location_ONB: Kent Ridge Campus

Posting Start Date: 21/08/2026

Job Description

Position Overview

Applications are invited for the post of Lecturer in Data Literacy at the National University of Singapore (NUS). The successful candidate will be appointed on a full-time, renewable contract under the NUS Educator Track scheme. The post is hosted by the Office of the Provost as administrative host and incubator for the programme, in collaboration with the Department of Mathematics and the Department of Statistics and Data Science.

Key Responsibilities

• Curriculum Design & Delivery: Assist Course Lead in directing the ongoing refinement of content, delivery and assessment for the university-wide general education course for the AI era, GEA1000 Quantitative Reasoning with Data. The course introduces foundational data sense-making and visualisation, drawing on authentic datasets from diverse fields, including science and engineering, healthcare, consumer insights, and public policy.
• Educational Innovation: Assist the Course Lead in integrating AI and other technologies into teaching pedagogies to enhance student mastery of core data concepts.
• Curriculum Alignment: Assist the Course Lead in collaborating with academic units across the university to ensure integration between this course and the higher-level data science and analytics courses.
  
  

Position Details

Start date: Immediate, or as soon as practicable, in semester 1 of AY2026/2027

Qualifications

Qualifications and Requirements

Essential

• PhD in Statistics, Mathematics, Physics, or closely related quantitative discipline.
  
  

Desirable attributes

• Technical Proficiency: Demonstrated expertise in data science, analytics, and practical applications, including proficiency in Excel, and programming languages such as Python or R.
• Higher Education Teaching: Proven track record of delivering broad-based, common curriculum undergraduate courses.
• Curriculum Development: Experience in course design, assessment development, and programmatic alignment.
• Pedagogical Innovation: Experience in leveraging educational technologies, including generative AI, for innovative teaching pedagogies to enhance learning outcomes.
• Scholarship of Teaching & Learning: Active interest or experience in evidence-based pedagogical research.
  
  

Please Submit The Following Documents

• Cover letter
• Curriculum vitae
• Scanned copies of academic degrees
• Teaching statement
• Two academic or professional referee letters
  
  

Req ID: 34151