Research Fellow
(Contract appointment ending 31 August 2026)
Job Description
- This position is for Sim Kee Boon Institute for Financial Economics (SKBI).
- Quantitative Framework Design & Index Construction:
- Translate qualitative theoretical factors into measurable quantitative metrics or factors.
- Develop a data-driven scoring and weighting methodology (e.g. weighted averages, conditional logit or other limited dependent variable models, Bayesian models, Hierarchical models etc.) to measure the "Readiness Level" of a specific sub-sector.
- Design and develop a rating system that allows for benchmarking across different deep tech verticals.
- Data Strategy, Sourcing & Engineering:
- Identify and integrate diverse data sources, including private investment databases (Crunchbase, PitchBook, Tracxn etc.), public records (ACRA, IPOS for patents), and talent networks (LinkedIn/Glints data).
- Collaborate with government partners (e.g. A*STAR, EnterpriseSG) and other partners in IHL's and industry to access and process relevant sector data.
- Build data processing and ingestion workflows to clean, transform, and interpret large datasets.
- Advanced Analytics & AI Implementation:
- Implement various AI/ML tools (e.g. AutoML, Natural Language Processing) to extract unstructured web data and perform data-driven sentiment analysis on ecosystem vibrancy and readiness.
- Use clustering algorithms to validate maturity stages and detect anomalies in the data.
- Collaborate with and report to SMU Principal Investigators to ensure the statistical validity and academic rigor of the framework.
Qualifications
- Preferably Master's or PhD in Economics, Statistics, Data Science, Finance, or a related quantitative fields.
- Statistical Analysis: Proficiency in R, Python, or Stata for econometric and statistical modeling and index construction.
- Data Engineering: Experience with SQL and data cleaning/transformation.
- AI/ML Familiarity: Experience with NLP (Natural Language Processing) and ML libraries (e.g. for sentiment analysis or clustering) is highly desirable.
- Tools: Familiarity with platforms like Databricks, Google Cloud BigQuery, or rapid data mining tools.
- Understanding of Innovation & Enterprise (I&E) metrics, venture capital (VC) terminology, and economic indicators.
- Ability to interpret data related to startup valuation, patent citations (IP), and talent flows.
- Translation: Ability to explain complex statistical concepts to non-technical stakeholders (government agencies, investors).
- Collaboration: Comfortable working in a hybrid environment between a government-owned institution (SGInnovate) and an academic institution (SMU).
Apply by 7th March 2026