Scientist, Data at Stanbic IBTC

To drive the design, development, adoption, and deployment of advanced analytics and AI solutions across all business units in the bank. The role will be responsible for collaborating with a team of data scientists in developing AI/ML models at scale, ensuring model governance and regulatory compliance, and supporting business units to embed AI into products, processes, and decision-making. The ideal candidate is both a strong technical leader and a strategic business partner.

• Support the development and execution of the bank’s Data Science & AI strategy in alignment with strategic business goals.
• Coordinate a team of data scientists and machine learning/AI developers in driving AI implementation across business and operations.
• Oversee end-to-end delivery of machine learning models: data sourcing, feature engineering, model development, evaluation, deployment, and monitoring.
• Develop predictive, prescriptive, and generative AI models for use cases such as credit scoring, fraud detection, personalisation, chatbot agents, agentic AI for payment and process improvements, etc.
• Define and implement scalable MLOps and AIOps pipelines for model deployment, monitoring, and lifecycle management.
• Lead Proof of Concept (PoC) and pilot initiatives for new AI capabilities.

Qualifications

• BSc in Computer Science, Statistics, Mathematics, Engineering, or related field.
• Master’s Degree and/or professional certifications (e.g., Azure ML, AWS ML, TensorFlow, Databricks, SAS, Agent orchestration, Microsoft Fabrics)can be added advantage.

Experience

• 5-6 years of experience in Data Science, Artificial Intelligence, or Machine Learning.
• Minimum 3 years in a senior technical role.
• Experience in banking or financial services is strongly preferred.
• Demonstrated experience with cloud platforms (particularly Microsoft Azure) and modern ML/AI tooling.

Behavioural Competencies:

• Adopting Practical Approaches
• Articulating Information
• Challenging Ideas
• Developing Expertise
• Documenting Facts
• Embracing Change
• Interpreting Data
• Managing Tasks

Technical Competencies:

• Big Data Frameworks and Tools
• Data Engineering
• Data Integrity
• IT Knowledge
• Stakeholder Management (IT)