Bachelor's degree in Statistics, Applied Mathematics, Data Science, Computer Science, Actuarial Science, Economics, Financial Engineering, Operations Research or another relevant quantitative discipline.
Master's degree in Data Science, Statistics, Artificial Intelligence, Business Analytics, Financial Engineering or a related discipline is an added advantage.
Relevant professional certification in data science, cloud analytics, machine learning, business intelligence, data management, risk or banking is an added advantage.
Minimum 3–5 years of relevant experience in banking analytics, data science, risk analytics, customer analytics or a closely related quantitative role.
Demonstrable experience delivering analytical solutions from business-problem definition through model development, deployment, adoption and benefits tracking.
Technical Skills
Python or R.
SQL.
Machine learning.
Statistical modelling.
Forecasting and anomaly detection.
Microsoft Power BI.
Microsoft Excel or equivalent analytical/visualisation tools.
Data-engineering fundamentals.
Version control.
Model deployment and monitoring.
Business Intelligence and data visualisation.
Data storytelling.
Data governance and model-risk principles.
Cloud-based analytics platforms.
Understanding of privacy and responsible use of data and AI.
Key Competencies
Strong analytical thinking and intellectual curiosity.
Sound professional judgement.
Ability to communicate complex analytical findings to technical, business and executive audiences.
Strong collaboration and stakeholder-influencing skills.
Attention to detail and disciplined documentation.
Commercial awareness and delivery focus.
Accountability for measurable outcomes.
Commitment to ethical, fair and responsible use of data and AI.
GK
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