• Identify and define use cases for data science and machine learning to solve business problems throughout Banking and Wealth Management Asia Pacific, and communicating these to senior and working level business partners across multiple functions.
• Work with the wider Analytics Centre of Excellence to deliver the use cases in a timely manner, including tracking results and communication to relevant business partners.
• Build and embed the data science and machine learning capability in Banking Analytics and Customer Relationship Management. Examples include but not limited to: neural networks to scientifically identify new event triggers, self-organizing maps to drive segmentation, gradient boosting to improve existing traditional response models, leveraging the adaptive learning capabilities in Pega, OCR to turn text into data to commercialese, trialing new tools, training and awareness for wider Analytics CoE.
• Staying abreast of new developments in the Machine Learning\AI industry, and identifying use cases within company to pilot new value-add tools\techniques.
• Understand, expand and leverage the external cloud analytics capability at company. Be the ambassador and promote use cases onto the platform. Deliver the use cases by leading\participating in scrums (Project Phileas), deliver use cases using cloud analytics capability.
• Identify and design solutions for cloud analytics to operationalize machine learning outputs directly onto the customer facing platforms.
• Develop strong working relationships with the wider RBWM community. These include, but are not limited to: Customer Value Management, Distribution, Retail Banking Products, Wealth, Data & Information Management, Business Performance, Finance, Technology.
• Ongoing vigilance in Information Security and other mandatory Compliance requirements, particularly cross-border, date security and privacy issues.

Requirement:

• Sufficient experience in analytics in the financial/banking sector or similar
• Bachelor degree in a relevant business discipline
• Demonstrated expertise in machine learning\AI techniques, e.g. neural networks, Natural Language Processing, collaborative filtering, self-organizing maps, OCR etc.
• Demonstrated expertise with machine learning tools and environments, e.g. Hadoop, AWS\Google cloud, Python. R, SAS etc.
• Demonstrated expertise working with high volumes of unstructured data from disparate sources
• Understanding of data strategy (storage, quality, governance, compliance, security, optimized processing etc.).
• Demonstrated experience at operationalizing outputs of data science approaches
• Expertise in the commercialization of new and existing data sources
• Strong communication skills at both working group and senior P&L owners
• Exposure and experience with customer lifecycle marketing
• Expertise in the design and implementation of event triggers and\or ‘always-on’ marketing to drive customer engagement.

上班地址：天河区