VP/AVP, Data Scientist, Global Financial Markets

About the role

DBS is hiring a VP/AVP Data Scientist for its Global Financial Markets team in Asia Central. The role develops AI, data analytics and automation solutions that support trading desks, helping generate better trade ideas, execute trades faster and more accurately, monitor and review trades more efficiently, and produce reports. It also aims to remove the day-to-day pain points of traders and sales staff.

Key responsibilities

• Understand traders' requirements and pain points and build AI solutions that improve efficiency or remove them.
• Run ad-hoc data analysis for trading and sales to drive economically valuable decisions and recommendations.
• Provide analytics solutions that give better insight into clients and track financial products.
• Train and implement predictive and machine learning models for trading, sales and COO initiatives, including large language models and agentic AI for trading and sales.
• Collaborate with business and operations teams to deliver high-performance analytics using Python, Cloudera, Dify, Spark, S3, SQL and Tableau.
• Perform exploratory statistical analysis and visualisation on datasets of all sizes, and support analytics initiatives with clear timelines.
• Build data ingestion pipelines, process and cleanse data, and set up dashboards and other visualisation tools.
• Stay updated on data science research and new tools, and help shift the business culture toward more data-driven decision-making.
• Lead, grow and develop junior team members, breaking down large problems into manageable milestones.

Required profile

• Degree, Honours, Masters or PhD in an analytics discipline.
• 2-5 years of experience in a similar data or analytics role is a plus, with good knowledge of global financial market products.
• Ability to handle data extraction, processing and analysis, and to understand and implement AI models.
• Strong written and interpersonal communication skills.

Required skills

• Python or related packages
• Predictive and machine learning modelling
• Cloudera, Dify, Spark, S3, SQL, Tableau