Job Advert Details
International Wealth and Personal Banking (IWPB) is our new global business combining Retail Banking and Wealth Management; and Global Private Banking, to become one of the world’s largest global wealth managers with USD1.4 trillion in assets.  Our dedicated colleagues serve millions of customers worldwide across the entire spectrum of private wealth, ranging from personal banking for individuals and families, through to business owners, investors and ultra-high-net-worth individuals.  We provide products and services such as bank accounts, credit cards, personal loans and mortgages, as well as asset management, insurance, wealth management and private banking, that best suit our customers’ needs. 

People responsibility: N
Report to: SVP Data and Analytics

Role Purpose
  • Data and Analytics is a key position supporting the commercial use of the outcomes of business analytics and governance control
  • Embed outcomes of business analytics and data science to optimize IWPB business decisions and activities such as acquisition, cross sell and win-back to deliver business objectives

Principal Accountabilities
  • Responsible for the continued development of the Analytical solutions and Business Intelligence solutions across a variety of use cases, including but not limited to customer acquisition, customer retention, product X-sell, pricing decisions and other business needs within IWPB Taiwan
  • Design data and analytics commercialization strategies across products and customer segments towards quantifiable outcomes, i.e. incremental revenue, cost save and improved customer experience
  • Build and implement AI Use Cases (Generative and Traditional AI) with a strong focus on customer benefits and the business impact to be achieve
  • Leverage global data and messaging capabilities and “no tech/ low tech” to transform operating model for faster speed to market. Interface with business and global analytics teams across the bank to formulate solutions 
  • Interface with business and global analytics teams across the bank to formulate solutions and product changes as informed by data insights from analytical models
  • Actively seeks to acquire or improve skills associated with his/her designated Discipline.
  • Clearly understands the operational and data risk and the associated governance and control framework
  • Manage operational risks, ensure adherence to Legal, Compliance, Regulatory and Information security standards and keep HSBC Data Ethics principles on top of everything. 
  • Document of process relevant to the activities; this would cover (1) Self-serve info management, (2) Business analytics management (3) Data governance and control framework and (4) Relevant processes
Qualifications
  • Experience in building and deploying Business Intelligence solutions, GCP solutions and AI/ML application on large amounts of data
  • Degree in Computer Science, Data Science, Applied Mathematics, Statistics, Machine Learning, or a related quantitative field
  • Strong communicator and good interpersonal skills
  • Ability to translate user requirements/ problem statements into technical solutions
  • Extensive hands-on experience in coding and modelling skills in GCP Big-query, SQL, Python, Shell script, UNIX command and XML/Json files processing
  • Strong front domain knowledge (e.g., retail banking products, sales productivity, marketing) to effectively identify the value of AI and analytics in the business context, preferably in the Wealth Management or Financial Industry context
  • Able to work with members of different skills and experiences. Comfortable to operate in a complex matrix structure with multiple stakeholders
  • Deep technical and data science expertise, including experience in the following:
    1. Statistical modeling (e.g., logistic regression, time series, CHAID, PCA), supervised machine learning (e.g., random forests, neural networks), unsupervised learning, design of experiments, segmentation/clustering, text mining, network analysis and graphical modelling, optimization, simulation
    2. Experience building in-production models, including associated scripting, error handling and documentation
    3. Understanding of trade-offs between model performance and business needs

Having these attributes would be advantageous:
  1. Strong experience in using open-source data for analyses
  2. Experience in one or more of data environments such as AWS, Google cloud or Hadoop
  3. Experience in one or more of Business Intelligence tools such as Qlikview, Qliksense or Cognos
  4. Good understanding of information management practices including information lifecycle management, data modelling, master data management and carrying out business, data and process analysis and design
  5. Proficiency in PEGA marketing or Teradata Customer Interaction Manager (CIM)

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