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Company Overview
A leading bank in Asia, headquartered in Singapore, with a history dating back to 1935. Initially named United Chinese Bank, it was founded by a group of businessmen to serve the merchant community in Singapore amidst the economic uncertainties of the Great Depression. It was later renamed in 1965.
AVP, Data Scientist (Global Markets), Data Management Office
Job Responsibilities
The role involves being a key member of the Enterprise AI team, engaging with users to develop and deploy Portfolio and Econometrics (P&E) capabilities and applications for various business and support units, ultimately delivering impactful outcomes.
The organization’s portfolio encompasses a wide range of assets and liabilities across different instruments, geographies, client segments, and products, categorized as market tradable and non-market tradable. The goal is to develop a suite of complementary portfolio and econometrics modeling, forecasting, management, and optimization capabilities. This will enable data science-driven strategic planning, scenario analysis, forecasting, and optimized portfolio shaping in a holistic, optimal, timely, systematic, and explainable manner.
Your main responsibilities include:
Collaborating with data scientists, data engineers, technology, and business/support units to evaluate, solution, and deliver P&E-driven business use cases.
Developing and successfully executing plans aligned with strategic project and business objectives.
Developing performant P&E capabilities through thoughtful research and experimentation.
Developing and deploying P&E applications with agile prototyping.
Integrating P&E capabilities and applications into the organization’s ecosystem.
Staying abreast of fast-emerging AI SOTA technological advancements, industry best practices, architecture, software resources, and paradigms.
Engaging and managing stakeholder interactions and expectations.
Providing deep expertise and advisory on P&E to diverse stakeholders.
Contributing to P&E-related benchmarking exercises.
Enriching skillsets and increasing exposure for data scientists, each will also contribute to GenAI capabilities delivery as a secondary, significant task.
Job Requirements
Minimum Postgraduate degree in Statistics, Mathematics, Engineering, Computer Science, or a related field, with the ability to reason quantitatively from first principles. AI-related thesis/publications on P&E topics are highly preferred.
Consistent success in framing rigorous and data-driven approaches to solve highly complex business use cases or academic problems.
Minimum 5 years of advanced data science working or research experience, preferably in banking/financial services/quant/consulting industry.
Self-motivated team player, able to ideate innovative solutions and work independently.
Highly skilled and successful in end-to-end Portfolio Optimization or Econometrics deployment, with 1.5 years of full-time experience. Demonstrable achievements are a must.
Strong and demonstrable expertise and experience in Deep Learning is a must.
Demonstrable expertise and experience in adjacent topics such as Reinforcement Learning, Causal Inferencing, Probabilistic Bayesian, and advanced Econometric Modelling are highly preferred.
Passion and some expertise and experience in GenAI such as LLM, Multimodal are highly preferred.
Proficiency in Python programming and large data handling. Highly skilled with common data science packages such as Pytorch and Market Optimization/Forecasting-related packages.
Experienced in general end-to-end data science solution delivery and assorted tools.
Ability to communicate analytic findings clearly and convincingly to business stakeholders.
Skilful in agile project management practices.
Experience with data visualization packages and/or tools is a plus.
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