VP/SVP, Data Scientist, Technology & Operations Data Chapter, Group Transformation

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