Data Scientist-Personalization

Assist in applying data mining techniques and conduct statistical analysis to large, structured and unstructured data sets to understand and analyse phenomena. Model business problems, discovering insights and opportunities through statistical, algorithmic, machine learning and visualisation techniques, working closely with clients, data and technology teams to turn data into critical information used to make sound business decisions.

• Leverage our client’s voices through personalised and contextually relevant conversations for client-led products and solutions design and development. Co-create, administer and enhance a client conversation portal (e.g. NBA) enabling bankers to have contextually relevant and meaningful conversations with clients.
• Collaborate with a multi-disciplinary insights and analytics team that partners in the design and delivery of personalised client conversations for Personal and Private Banking Clients, country.
• Build, maintain and enhance statistically robust and accurate campaign models to facilitate conversations, take-up optimisation across acquisition, retention, right-sell, and cross-sell interventions for all PPB segments.
• Design, develop and deploy scalable data pipelines and machine learning solutions leveraging cloud-based platforms (e.g. Azure, AWS) to support real-time and batch analytics use cases.
• Operationalise machine learning models through robust MLOps practices, including model deployment, monitoring, versioning, and automated retraining to ensure consistent performance and business value delivery.
• Apply advanced machine learning and artificial intelligence techniques (including NLP, recommender systems and Generative AI) to enhance personalisation, customer engagement and decisioning frameworks.
• Design and implement experimentation frameworks (e.g. A/B testing, uplift modelling, causal inference) to measure and optimise customer and commercial outcomes across campaigns and journeys. Ensure adherence to data governance, model risk management, and responsible AI principles, including explainability, fairness, and regulatory compliance in all analytics solutions.
• Translate complex analytical outputs into clear, compelling and actionable insights for business stakeholders, enabling data-driven decision making at all levels of the organisation.