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HSBC is one of the largest banking and financial services organisations in the world, with operations in 64 countries and territories. We aim to be where the growth is, enabling businesses to thrive and economies to prosper, and, ultimately, helping people to fulfil their hopes and realise their ambitions.

We are currently seeking an experienced professional to join our team in the role of Assistant Vice President(CRM) - GAC WPB AMH DATA ANALYTICS.

Principal responsibilities

The role is part of HSBC Global Data Analytics team and is responsible for the management of all customer information and provision of analytical and data science deliverables for customer and banking initiatives and to support all RBWM activities. The individual is also expected to identify key trends that arise from the insights of customer data and work with other areas in RBWM to ensure they are taken account of.The suitable candidate should be a self-driven person and a leader by nature in taking full ownership of the deliverables. One should be technically profound and fully loaded with business acumen and communication skills. One will work hands-on with the Enterprise Data Warehouse and Jupyter environment for data and modeling process, and are required to deliver the results in a logical and ethical way to the stakeholders.Design, formulate, and implement relevant solutions for the identified analytics project and convert analytical findings/solutions that will lead to achieving business goals (revenue growth, anti-attrition, and increasing customer engagement, etc.). During the process, closely collaborate with relevant teams/parties to obtain the supports required for these tasks.Use company provided software tools to extract, collect, manipulate (e.g. subset and merge) raw data to prepare for further analysis. Then apply the appropriate analytics frameworks, statistic procedures or machine learning algorithms to analyze the data in order to provide actionable analysis findings. Interpret and present quantitative information as well as the findings in proper context so as to help in the investigations and decision making of the business.To design and develop robust, relevant data mining solutions – descriptive or predictive, the incumbent should flexibly and innovatively apply diverse modelling methods including (but not limited to): decision tree, clustering, logistic regression, gradient boosting, neural network, random forest, support vector machine etc.. Manage the timely delivery of output and effectively communicate with all stakeholders.