Senior Associate, Machine Learning Engineer, Investment & Trading Technologies, Technology & Operations
DBS Bank
- Singapore
- Permanent
- Full-time
- Work with quants, traders, and dealers to solve complex optimization problems using advanced machine learning and quantitative methods.
- Conduct research and literature reviews to assess quantitative algorithms and models, ensuring the adoption of optimal methodologies.
- Implement and train AI/ML models, optimizing algorithm and system efficiency through GPU distributed computing and concurrent programming.
- Create reusable libraries/ microservices, deploy machine learning ecosystems, and integrate subsystems as necessary.
- Proficient in the data science project life cycle, demonstrated by a proven track record of working with structured, semi-structured, and unstructured data.
- Deep understanding and application of various machine learning concepts, their mathematical underpinnings, and trade-offs.
- Exceptional programming skills in Python (Or demonstrable ability to pick up new languages) and SQL variants, with knowledge of design patterns, code optimization, and object-oriented design.
- Familiarity with software development best practices and tools such as Agile methodologies, Jira, Jenkins, and Git.
- Demonstrable expertise in econometrics, statistical modelling, time-series analysis, causal inference, and their applications to pricing and marketing domains.
- Hands-on experience in designing and executing digital experimentation and hypothesis testing, including A/B testing, bandit-based experiments, and multivariate analysis.
- Experience with language models, RAG concepts, opensource generative AI (GenAI) frameworks and prompt engineering principles.
- Experience with machine learning applications in financial markets with a solid understanding of market dynamics, and key drivers.
- Experience developing reinforcement learning models and frameworks at scale.
- Strong research background with experience conducting literature reviews and prototyping fit-for-purpose custom models.
- Experience building scalable machine learning system architectures (microservice, distributed, etc.) and big-data pipelines in production.