Customer Analytics & Data Science Manager (MIS & Modeling)
Bank Of China (Hong Kong) Limited
Hong Kong
source : eFinancialCareers Ltd.

Customer Analytics & Data Science Manager (MIS & Modeling) Responsibilities :

  • Perform customer segmentation and profile analysis through advanced statistical modeling / data mining / AI & ML technologies to provide insight for segment strategy and identify business opportunities
  • Develop and enhance propensity / predictive models to support CRM strategies related to change of customer behavior or specific event trigger, eg.
  • customer acquisition, upgrade and retention

  • Compile MIS reports and conduct analysis across customer, product and channel perspectives to identify potential customer and deliver actionable insights and recommendations to business units for developing portfolio and product strategy
  • Assist business units in applying propensity / predictive model and customer segmentation model in marketing campaign to drive business performance, evaluate and enhance model effectiveness to improve campaign efficiency
  • Develop and enhance MIS report automation to help improve campaign efficiency
  • Requirements :

  • Degree or above with major in quantitative, numerical science or related discipline
  • 3+ years' experience relating to data analytics
  • Proficiency in SAS, Python, R or similar analytically oriented programming languages, experience in model development covering segmentation, acquisition, attrition, cross-selling and related applications
  • Experience in Banking, Financial Services or FinTech industry preferred
  • A mature self-starter with strong drive, motivation, committed with good business sense and strong understanding of the personal banking market
  • Excellent interpersonal and communication skills with excellent written and spoken English and Chinese, knowledge of Mandarin would be a definite advantage
  • Less experience will be considered as our Deputy / Assistant Customer Analytics & Data Science Manager
  • Job-ID : 498179 5QD762JwK495KkcB Datum der Veröffentlichung : 22 Jan 22


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