Research Associate, Data Science, ROSA
- Singapore | Closing On 14 Nov 2020
contract ending 30 June 2025
Singapore Management University is a place where high-level professionalism blends together with a healthy informality. The 'family-like' atmosphere among the SMU community fosters a culture where employees work, plan, organise and play together – building a strong collegiality and morale within the university.
Our commitment to attract and retain talent is ongoing. We offer attractive benefits and welfare, competitive compensation packages, and generous professional development opportunities – all to meet the work-life needs of our staff. No wonder, then, that SMU continues to be given numerous awards and recognition for its human resource excellence.
- This position is for Centre for Research on Successful Ageing (ROSA).
- Data cleaning, processing and publication of updated Singapore Life Panel (SLP) dataset conducted in STATA.
- Update of SLP data documentation (e.g. data dictionaries, briefings for derived variables).
- Assist with literature reviews, and staying up-to-date with academic literature on the treatment of longitudinal data.
- Improve quality of SLP dataset by way of construction of attrition weights and constructing new derived variables.
- Publication of academic papers (either individually on statistics / data science or co-authoring as the data scientist alongside researchers in economics / sociology / psychology).
- Contribution to research collaborations with government ministries and agencies.
- Providing advice to researchers on the data that is available in the SLP and its proper treatment.
- Quarterly preparation of descriptive summary statistics for sharing at research team meetings, distribution in newsletters, or for sharing with the media.
- Master’s degree is preferred in Data Science / Statistics / Mathematics.
- Strong skills in data processing, analytics and modelling using Stata is essential.
- Experience with longitudinal datasets is desirable.
- Detailed knowledge of Excel.
- Additional proficiency in SPSS or R will be highly valued.
- Good written and verbal communication skills are required.
- Familiarity with published research in quantitative methods, particularly longitudinal panel data.
- Publications based on these methods would be advantageous.