Job Purpose

The Data Scientist is responsible for designing, developing, validating, deploying, and continuously improving data science and machine learning solutions that deliver measurable business value in banking, while complying with the Bank’s AI governance, model risk management, data governance, information security, privacy, and regulatory requirements. The role ensures models and analytical solutions are accurate, explainable, fair, secure, well-documented, and fit for purpose throughout their lifecycle.

Principle Responsibilities

• Design, develop, and implement predictive, prescriptive, and optimization models for priority banking use cases such as fraud detection, credit risk assessment, collections, customer analytics, and operational efficiency.
• Translate business problems into data science use cases, define success criteria with stakeholders, and ensure proposed solutions align with approved business objectives and governance requirements.
• Perform data exploration, feature engineering, model training, testing, and performance evaluation using sound statistical and machine learning techniques.
• Prepare complete model documentation, including business rationale, methodology, assumptions, data sources, feature definitions, limitations, performance metrics, and implementation considerations, to support review, approval, audit, and regulatory scrutiny.
• Ensure models are developed and maintained in line with the Bank’s AI governance framework, model risk management standards, data governance requirements, responsible AI principles, and applicable regulatory obligations.
• Support model validation and approval processes by providing transparent documentation, reproducible development artefacts, evidence of testing, and clear explanations of model logic, outputs, and limitations.
• Assess and mitigate risks relating to model bias, unfair outcomes, data quality, privacy, explainability, robustness, and misuse, and escalate material issues through the appropriate governance channels.
• Collaborate with Data Engineering, MLOps, IT, Risk, Compliance, Information Security, Internal Audit, and business teams to ensure controlled deployment, integration, monitoring, and change management for analytical solutions.
• Monitor models and analytical solutions in production for performance, stability, drift, fairness, and operational effectiveness, and recommend recalibration, retraining, rollback, or retirement where required.
• Maintain version control, traceability, and audit trails for datasets, code, experiments, model versions, approvals, and production changes in accordance with internal standards.
• Apply appropriate controls for data confidentiality, customer privacy, access management, and secure handling of sensitive information throughout the model lifecycle.
• Contribute to model inventories, periodic reviews, performance reporting, and governance forums by providing timely updates on model status, issues, risks, and remediation actions.
• Support experimentation with advanced techniques such as time-series forecasting, natural language processing, and deep learning where justified by business need, data readiness, and governance approval.
• Promote a culture of responsible, ethical, and evidence-based use of AI and analytics across the organisation, including knowledge sharing and adherence to approved standards and practices.

Qualifications Required

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related field.
• Minimum of 3 years experience in machine learning, statistical modeling, or data analysis.
• Professional certifications in Azure AI, Data Science, MLOps, and Responsible AI are mandatory.
• Master’s degree in Data Science, Artificial Intelligence (AI), Machine Learning, Statistics, Business Analytics, or an MBA with a specialization in Analytics will be an added advantage.
• Demonstrated experience in machine learning, data engineering, AI governance, and business value realization.
• Proficiency in Python, SQL, and machine learning frameworks such as Scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Strong understanding of statistics, probability, and data science principles.
• Experience with data visualization tools such as Tableau, Power BI, Matplotlib, and Seaborn to communicate insights effectively.
• Familiarity with cloud-based machine learning solutions on AWS, Azure, or GCP

• Design, develop, and implement predictive, prescriptive, and optimization models for priority banking use cases such as fraud detection, credit risk assessment, collections, customer analytics, and operational efficiency.
• Translate business problems into data science use cases, define success criteria with stakeholders, and ensure proposed solutions align with approved business objectives and governance requirements.
• Perform data exploration, feature engineering, model training, testing, and performance evaluation using sound statistical and machine learning techniques.
• Prepare complete model documentation, including business rationale, methodology, assumptions, data sources, feature definitions, limitations, performance metrics, and implementation considerations, to support review, approval, audit, and regulatory scrutiny.
• Ensure models are developed and maintained in line with the Bank’s AI governance framework, model risk management standards, data governance requirements, responsible AI principles, and applicable regulatory obligations.
• Support model validation and approval processes by providing transparent documentation, reproducible development artefacts, evidence of testing, and clear explanations of model logic, outputs, and limitations.
• Assess and mitigate risks relating to model bias, unfair outcomes, data quality, privacy, explainability, robustness, and misuse, and escalate material issues through the appropriate governance channels.
• Collaborate with Data Engineering, MLOps, IT, Risk, Compliance, Information Security, Internal Audit, and business teams to ensure controlled deployment, integration, monitoring, and change management for analytical solutions.
• Monitor models and analytical solutions in production for performance, stability, drift, fairness, and operational effectiveness, and recommend recalibration, retraining, rollback, or retirement where required.
• Maintain version control, traceability, and audit trails for datasets, code, experiments, model versions, approvals, and production changes in accordance with internal standards.
• Apply appropriate controls for data confidentiality, customer privacy, access management, and secure handling of sensitive information throughout the model lifecycle.
• Contribute to model inventories, periodic reviews, performance reporting, and governance forums by providing timely updates on model status, issues, risks, and remediation actions.
• Support experimentation with advanced techniques such as time-series forecasting, natural language processing, and deep learning where justified by business need, data readiness, and governance approval.
• Promote a culture of responsible, ethical, and evidence-based use of AI and analytics across the organisation, including knowledge sharing and adherence to approved standards and practices.

• Python
• SQL
• Scikit-learn
• XGBoost
• TensorFlow
• PyTorch
• Tableau
• Power BI
• Matplotlib
• Seaborn
• AWS
• Azure
• GCP

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related field.
• Professional certifications in Azure AI, Data Science, MLOps, and Responsible AI are mandatory.
• Master’s degree in Data Science, Artificial Intelligence (AI), Machine Learning, Statistics, Business Analytics, or an MBA with a specialization in Analytics will be an added advantage.
• Demonstrated experience in machine learning, data engineering, AI governance, and business value realization.
• Strong understanding of statistics, probability, and data science principles.