Investment Insights Group
We use advanced quantitative techniques and cutting-edge technological tools to generate insights that drive outstanding investment outcomes for GIC.
Digital Products
You will be part of a team of alpha technologists developing platforms for investment teams and quantitative researchers, allowing GIC to harness our differentiated quantitative methods at scale. We are about to embark on a journey to bring in Large Language Models into GIC and are looking for a suitable candidate to join us as a Lead Data Scientist to do so.
What impact can you make in this role?
You will be part of a team that uses advanced quantitative methods, alternative data, and technology to generate superior investment performance.
What will you do as a Lead Data Scientist?
- The data science team is looking for a scientist with extensive experience in Deep Learning/Natural Language Processing on financial documents and news articles.
- The scientist's primary job responsibilities are to develop Large Language Models and NLP solutions to help traders, portfolio managers and risk managers to identify investment opportunities in text data.
- Lead the system design and pipeline workflow of Large Language Models on text documents.
- Identify and track emerging trends based on NLP that can quickly surface investment opportunities.
- Deploy Large Language Models and NLP models into production and monitor their performance.
- Independently develop accurate NLP sequence models or modify transformer models for text understanding at varying scales (document, paragraphs and sentence levels and across time).
- Independently conduct research on idea generation in the investment process.
What Qualifications or skills should you possess in this role?
- Formal training in quantitative discipline (Computer Science, Physics, Mathematics, Finance, Engineering, or other quantitative discipline), and experience in utilizing those skills in an applied research environment. Master’s or PhD degree is required.
- Natural Language Processing, Signal processing and Sequence modelling experiences using Deep Learning-based Models are highly desirable.
- Strong in software engineering and best practices (e.g. code smell, single-responsibility principle, functional programming).
- Strong in statistical fundamentals, experience conducting hypothesis testing and sampling highly desirable, with demonstrated programming skills highly desirable.
- Understanding of financial markets, including multi-asset research is a plus.
- Experience in developing Deep Learning Models and deploying them in production.
- Understanding of data available in the investment management industry and experience in managing and accessing such data to support research efforts.
- Ability to work efficiently and multi-task effectively in a fast-paced and team-oriented environment. This will include end-to-end research project work under from data gathering to hypothesis testing and model implementation.
- 5-8 years of hands-on engineering experience with software projects related to data science or machine learning.
- Good communication skills. Able to explain results of their work clearly to stakeholders.