Description

Stanbic Bank is hiring a Data Scientist-Personalization to 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.

Qualifications

First Degree Mathematical Sciences, Information Technology or related from a recognised Institution. Masters Degree in Business Commerce

5–7 years proven experience in deep quantitative, analytics, and modelling environments with a proven track record of delivering both customer and commercial outcomes.

Proven track record in customer insights and analytics environment with end-to-end accountability on translating data into insights and translating the derived insights into actionable customer conversations delivering both customer and commercial outcomes.

Experience in working with unstructured data (e.g. streams, images) and understanding of data flows, data architecture, ETL and processing of structured and unstructured data. Experience in designing and managing data solutions in cloud environments (e.g. Azure, AWS), including distributed data processing and scalable analytics architectures.

Experience in implementing machine learning models into production environments, including API deployment and integration into enterprise systems. Experience with common data science toolkits, MLOps frameworks, and data visualisation tools.

Relevant cloud and AI certifications (e.g. Azure AI Engineer, AWS Machine Learning Specialty) will be an added advantage.

Additional Information

Technical Competencies:

• Data Analysis
• Database Administration
• Data Integrity
• Knowledge Classification
• Research & Information Gathering
• Cloud Computing (Azure/AWS)
• Machine Learning Operations (MLOps)
• Advanced Machine Learning & AI Techniques
• Data Engineering & Pipeline Development
• Experimentation & Decision Science
• Responsible AI & Model Governance
• Software Engineering Practices

Behavioural Competencies:

• Adopting Practical Approaches
• Articulating Information
• Challenging Ideas
• Checking Things
• Examining Information
• Exploring Possibilities
• Interacting with People
• Product Thinking and Customer-Centric Design
• Data Storytelling and Business Influence
• Strategic Problem Solving
• Continuous Learning and Innovation

• 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.
• Leverage 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.

• Data Analysis
• Database Administration
• Data Integrity
• Knowledge Classification
• Research & Information Gathering
• Cloud Computing (Azure/AWS)
• Machine Learning Operations (MLOps)
• Advanced Machine Learning & AI Techniques
• Data Engineering & Pipeline Development
• Experimentation & Decision Science
• Responsible AI & Model Governance
• Software Engineering Practices
• NLP
• Recommender Systems
• Generative AI
• A/B testing
• Uplift modelling
• Causal inference
• Data visualisation tools

• First Degree Mathematical Sciences, Information Technology or related from a recognised Institution.
• Masters Degree in Business Commerce
• 5–7 years proven experience in deep quantitative, analytics, and modelling environments with a proven track record of delivering both customer and commercial outcomes.
• Proven track record in customer insights and analytics environment with end-to-end accountability on translating data into insights and translating the derived insights into actionable customer conversations delivering both customer and commercial outcomes.
• Experience in working with unstructured data (e.g. streams, images) and understanding of data flows, data architecture, ETL and processing of structured and unstructured data.
• Experience in designing and managing data solutions in cloud environments (e.g. Azure, AWS), including distributed data processing and scalable analytics architectures.
• Experience in implementing machine learning models into production environments, including API deployment and integration into enterprise systems.
• Experience with common data science toolkits, MLOps frameworks, and data visualisation tools.
• Relevant cloud and AI certifications (e.g. Azure AI Engineer, AWS Machine Learning Specialty) will be an added advantage.