A fantastic opportunity to join one of the world’s leading financial services providers as a regional specialist leader in their analytics and data science team that covers 14+ markets in Asia. This position will play a critical role in developing cutting edge data science solutions for key stakeholders across business units in Asia.
About the Company Culture:
Multicultural and rapidly growing digital focus. This role offers the opportunity to work with the top talents in the analytics and data science fields in Hong Kong.
Responsible for driving a journey to leverage AI, ML, Deep Learning (DL), Natural Language Processing (NLP) to better insurance processes and business model benefiting both internal and external stakeholders, creating the next generation insurance platform.
- Play a key role in the success of integrating ML/AI concepts into the business and provide hands-on development of AI/ML solutions to create a measurable impact on cost reduction and/or revenue.
- Champion the launch and implementation of Proof of Concept projects and subsequently industrialize the successful concepts across business units in the APAC region.
- Engage business leaders to discuss the business benefits that ML and AI can provide.
- Identify opportunities to leverage ML and AI technology with Group and Business Units key stakeholders
- Create and manage use cases to apply ML and AI concepts
- Develop AI and ML models and algorithms to solve business problems
- Document best practices and solution frameworks. Replicate and scale solutions to markets with common/similar business needs
- Identify and build strategic alliance with influencing and respected partners within the ML & AI community to enrich the internal understanding of this emerging technology
- At least 5 of years working experience, with extensive hands-on experience in developing AI and Machine Learning solutions;
- Proven hands-on experience in the use of at least one advanced data analysis platform (e.g. Python, R, SAS);
- Exposure to all stages of data analytics project lifecycle (scope definition, data requirements, extraction, exploration, transformation, solution development, tracking);
- Demonstrable understanding of data quality risks and ability to carry out necessary exploratory data analysis and quality checks to validate results obtained;
- Knowledge of a variety of machine learning techniques (clustering, decision tree, random forest, artificial neural networks, etc.);
- Excellent relationship management, strong team building, and the ability to work across business units and functions to drive positive business outcomes;