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VP/SVP, Data Scientist, Technology & Operations Data Chapter, Group Transformation
VP/SVP, Data Scientist, Technology & Operations Data Chapter, Group Transformation
As a Senior Data Scientist at DBS Bank, you will lead complex data science projects, develop and deploy advanced machine learning and generative AI models, and establish scalable data science pipelines. You will be responsible for driving data-driven strategies and AI/ML solutions across Technology & Operations, ensuring alignment with the bank’s AI industrialization agenda and robust model governance.
About the role
About The Role
As a Senior Data Scientist, you will play a pivotal role in advancing data-driven strategies to drive business impact and enhance customer experiences. You will lead complex data science projects, provide best-in-class analytics and AI / ML solutions that drive decision-making across T&O, while ensuring alignment with DBS’ AI industrialization agenda.
Key Responsibilities
- Lead the development and execution of AI / ML and generative AI strategies and capabilities, aligning with the bank’s AI industrialization agenda
- Design, develop and deploy advanced machine learning models and algorithms to support T&O’s strategic priorities, including establishing scalable data science pipelines
- Ensure robust model governance, including performance monitoring, validation, and re-calibration, in line with governance standards and best practices
- Support the implementation of AI industrialization in T&O and drive reduced effort for end-to-end deployment of models by supporting the creation and usage of reusable components across the AI / ML Lifecycle
- Collaborate with different business stakeholders across Operations, Data Chapters and Technology to define and prioritize AI / ML initiatives that drive impactful business outcomes
- Engage with senior leadership and business heads to identify opportunities where data science can deliver significant value
- Stay abreast of industry trends, emerging technologies, and best practices in data science and drive knowledge sharing across AI / ML use cases in T&O and across Data Chapters
Key Requirements
- PhD or Master’s or equivalent degree in Computer Science / Engineering / Mathematics, or other related fields, preferably in the areas of Machine Learning, Data Mining, Deep Learning, or equivalent quantitative fields
- Minimum 10 years of data science experience in industry (ideally banking, ecommerce, telecoms, retail, or technology firms) and/or academia with demonstrated track record of innovative research and insight generation and implementation of insights into tools/processes delivering front-end business outcomes
- Minimum 5 years of experience working with large datasets (including structured, semi-structured and unstructured data), building, and implementing various machine learning models or applications
- Excellent modelling background including but not limited to classification, clustering, recommender systems, deep learning, reinforcement learning, natural language processing etc
- Proven track record of deploying machine learning solutions into production environments, integrating them into operations, and implementing continuous model performance monitoring
- Familiar with software development best practices and tools
- Experience in implementing generative AI models is a plus
- Strong communication, interpersonal and stakeholder management skills
Skills
AiMLGenerative AiMachine LearningData scienceData PipelinesModel GovernanceStructured DataSemi structured DataUnstructured DataClassificationClusteringrecommender systemsDeep LearningReinforcement LearningNatural Language ProcessingSoftware Development