• Bachelor’s degree in Data Science, Statistics, Economics, Finance, Computer Science, or a related field.
• 2–5 years of experience as a Data Analyst, preferably within banking or financial services.
• Strong understanding of corporate banking products (loans, deposits, trade finance, treasury, payments).
• Hands-on experience with data modeling techniques (e.g., star/snowflake schema, ER diagrams) and data mapping across systems.
• Proficiency in ETL tools and processes, and experience working with structured and semi-structured data.
• Familiarity with statistical analysis tools (Python, R) and SQL for data querying and transformation.
• Knowledge of risk, compliance, and regulatory frameworks in banking (e.g., Basel, AML, KYC).
• Excellent communication skills to translate technical data into business insights.
Preferred Attributes:
• Experience with data warehousing and big data platforms (e.g., Snowflake, Hadoop, Spark).
• Exposure to machine learning techniques for credit scoring, risk modeling, or client segmentation.
• Strong business acumen with the ability to link data insights to revenue, cost, and risk metrics.
• Experience with BI tools (e.g., Power BI, Tableau) for dashboarding and visualization.
• Ability to work effectively in cross-functional teams within a fast-paced banking environment.
