Business Function
As the leading bank in Asia, DBS Consumer Banking Group is in a unique position to help our customers realise their dreams and ambitions. As a market leader in the consumer banking business, DBS has a full spectrum of products and services, including deposits, investments, insurance, mortgages, credit cards and personal loans, to help our customers realise their dreams and aspirations at every life stage. Our financial solutions are not only the best in the business – they were made just right for you.
Responsibilities
A successful candidate will be a key contributor to one of DBS Bank's most ambitious and exciting digital transformation initiatives, which consists of using Artificial Intelligence and Machine Learning (AIML) to provide ultimate experience to our millions of consumer banking customers. Your roles and responsibilities as a data scientist would encompass several areas:
- Formulate and develop strategic data science engines leveraging cutting-edge AIML algorithms such as Bayesian inference, reinforcement learning, causal AI, generative AI, etc.
- Work with large-scale, distributed data science and machine learning tools/frameworks such as Spark, Torch/Tensorflow, HuggingFace, Microsoft Synapse, etc.
- Apply best software engineering practices for development, testing, and deployment of AIML models in both staging and production environments
- Collaborate with multiple, regional stakeholders to onboard business use cases, capture the technical requirements, and share insights generated by AIML models
- Implement responsible AI frameworks, such as data quality profiling, model explanability, fairness assessment, and model drift monitoring, to guide the development and use of AIML models
Requirements
The following requirements are highly essential/desirable for the abovementioned data scientist role:
- Excellent academic qualification (PhD or Master degree preferred) in Computer Science, Physics or Mathematics
- Strong technical skills in large-scale distributed processing technologies such as Spark, Hive/Hadoop and distributed machine learning
- Experience formulating, developing and deploying hybrid AIML and rule-based recommender systems, preferably with prior exposure to banking use cases
- Exposure to advanced data science methodologies such as Bayesian inference, natural language processing, generative AI, and reinforcement learning
- Strong foundation in data structure, algorithm complexity, and best software engineering practices
- Experience with model lifecycle management practices, such as unit and integration tests, continuous integration and delivery (CI/CD), and model artefact management
- Strong understanding of responsible AI principles and practices, such as model monitoring, fairness and explanability
- Proficient in practical machine learning principles such as cross-validation, hyperparameter optimisation, and feature engineering/selection
- Self-motivated team player who enjoys working in a dynamic, fast-paced environment as well as collaborating with people from diverse backgrounds
- Adaptable, creative, decisive, resilient as well as capable of managing multiple projects
- Excellent interpersonal and presentation skills, capable of communicating effectively with both technical and non-technical stakeholders
- Exposure to (co-)authoring research/technical publications would be a plus point
Apply Now
- We offer a competitive salary and benefits package and the professional advantages of a dynamic environment that supports your development and recognises your achievements.
