DATA SCIENTIST

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.

Requirements

• two position

Qualifications

• 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.
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Skills