About the role
AI summarisedVP-level quantitative analytics lead in Corporate Treasury at a bank, focusing on portfolio optimization and balance sheet management using machine learning, generative AI, and quantitative models.
BusinessFull-timeGeneral
Key Responsibilities
- To research, develop, and operationalise algorithms/models to address specific business problems around the areas of Portfolio Management & Balance Sheet Management
- To develop quantitative models and strategies around asset allocation & portfolio optimisation, specifically in forecasting and projections
- To understand business requirements from key stakeholders and develop solutions through experimentation and in-depth study of various algorithms/ machine learning models
- To leverage on Generative AI and LLMs for analysis and developing solutions
- To operationalize model pipelines into re-usable libraries for the team
- To automate models by leveraging on existing tools and frameworks
Requirements
- Individual with a strong interest and natural curiosity in exploring various technical domains in the areas of optimisation and machine learning.
- 7-10 years of experience in applied statistical learning, machine learning and data science with actual outcomes.
- Experience in quantitative strategies, research & markets is strongly preferred.
- In-depth knowledge and hands-on experience in generative AI, LLMs & deep learning.
- Deep understanding of statistics, math and optimisation theory.
- Excellent written and communication skills, especially one who can explain how machine learning models and statistical learning algorithms are applied.
- A keen learner who desires to learn and acquire new knowledge both in business and technical domains, as well as to acquire proficiency in new tools, languages and techniques.
- A strong team player who can contribute as an individual and drive projects.
- Extremely proficient in software development, with in-depth knowledge of analytical toolkits & libraries available in Python.
- Master's or Ph.D in Computer Science, Statistics, Math, Engineering or other quantitative disciplines.
- Candidates with strong statistics & math backgrounds would be highly preferred.