Data & Analytics Manager
HSBC
- Kuala Lumpur
- Permanent
- Full-time
- Manage end-to-end data & analytics initiatives from ideation, design & development, deployment & commercialization in an agile working model; able to manage / influence a wide range of partners / stakeholders from technology, business owners as well as senior management.
- Partnering with diverse business functions and HSBC business entities as well as country / regional analytics teams to identify & prioritize opportunities where Data & Analytics can add value; and provide analytics solutioning / quick prototypes that are relevant to business needs in the areas of (not limited to) persona development, signal / propensity based targeting, data-driven personalization & messaging capability, full cycle leads targeting, execution and tracking capability, sales management analysis and process improvement, etc.
- Mine through large data sets to uncover trends, insights, and opportunities and apply relevant analytical methods / algorithms to get actionable insights from HSBC's internal (both structured and unstructured) and external data sources.
- Conduct exploratory data mining and analysis to understand customer behaviours, experience, engagement and uncover opportunities.
- Design end to end ML/AI solutions: from understanding business requirements, data discovery and extraction, model development and evaluation, to production pipeline implementation.
- Deploy new or tweak existing business intelligence dashboards to help organization move towards self-sufficiency when making decisions based on data.
- Managing the Communication Messaging platform end to end, from campaign elaboration, campaign implementation and campaign tracking.
- Execution and management of various reporting (internal and regulatory), campaign roll-outs and campaign fulfilments from relevant internal stakeholders.
- Lead projects related to digital analytics, modelling, data investigation, data development and opportunity identification. Provide recommendation for business adoption and commercialization.
- Support Data Engineering pipelines to deploy new data sources to Google Cloud. Activities include providing ETL logic to Data Engineering team, performing logic rectification, executing various testing activities like UAT, regression testing, ensuring issues/defects are flagged off appropriately.
- Work with key stakeholders implementing control and risk management frameworks on data & analytics processes.
- Experience in the Financial Service or Banking sector; or similar experience in analytics consulting or managing digital services and products.
- Minimum 3 years of experience of using Data Science or Machine Learning models to solve complex business problems.
- Proficiency in SAS, SQL, Python, R and/or other Data Science Languages. Knowledge and exposure to cloud analytics platform such as GCP, AWS, Hadoop, Azure.
- Demonstrated understanding of Tag Management & Analytics on Paid Advertising, advanced triggers, custom tag/variable templates, tag sequencing, troubleshooting and preview, GTM staging/production environments, and advanced custom dataLayer-powered triggers for custom site interactions.
- Degree qualification in a relevant business discipline, Economics, Marketing, Math, Statistics or other quantitative fields of study
- Experience delivering a variety of ad hoc extracts, self-service visualisation, and complex automated reporting for business stakeholder.
- Enthusiasm for proactively seeking, exploring and developing use cases for new data and/or tools/wider industry trends.
- Strong business acumen with high ability to diagnose and articulate ambiguity in business problems, and out-of-the box thinking to drive improved business performance.
- Excellent written and verbal communication skills. Ability to develop and effectively communicate complex concepts and ideas.
- Experience in solving business problems end to end from conceptualisation to final insight delivery & benefit quantification.
- Strong analytical skills with hands on experience in various statistical modelling algorithms and its applications to business problems. Knowledge of forecasting, finance modelling techniques and machine learning algorithms is required.
- Proven experience leading analytics, Customer Relationships Management, and business intelligence organizations in the Retail Banking industry
- Strong team player working across senior levels of the organization to drive and influence change.