AVP / VP, Senior Data Analyst, AGMD-Research & Innovation Lab (RIL)

Job Responsibilities:

• Collaborate with teams of Business Analyst and Data Analyst in conducting comprehensive analyses of banking data, including customer transactions, product performance, customer behaviour, market trends, etc.
• Develop and implement data-driven strategies to optimize new product creation, customer acquisition, customer retention (anti-attrition), and cross-selling opportunities within banking portfolio.
• Collaborate with business units and senior leadership to identify key performance metrics, KPIs, and benchmarks to track and measure business performance.
• Conduct hands-on and utilize statistical techniques, predictive modelling, machine learning etc, to forecast business trends (including funding and lending), assess risk such as credit risk, and support decision-making processes.
• Partner with IT and data engineering teams to ensure data quality, integrity, and availability for analysis purposes.
• Provide strategic recommendations based on data insights to enhance product offerings, pricing strategies, and operational efficiency.
• Stay attuned on industry trends, regulatory changes, and competitive landscape affecting the banking sector in relevant countries, to inform data analysis and strategic initiatives.
• Adopt data visualization and present findings and recommendations to senior executives and stakeholders in a clear and compelling manner and influence strategic decisions.

Job Requirements:

• Bachelor’s degree in Statistics, Mathematics, Economics, Business Administration, or a related field.
• Min. 8 years of experience in data analysis, business intelligence, or related roles within the banking industry.
• Strong proficiency in SQL, Python/R, and data visualization tools (e.g., Tableau, Power BI) for analyzing large datasets and creating insightful visualizations.
• Proven track record of delivering actionable insights and recommendations through data analysis to drive business growth and operational efficiency in banking.
• Deep understanding of retail banking products, services, customer lifecycle, and regulatory environment.
• Familiarity with data governance principles and practices, ensuring compliance and data integrity in analytical projects.
• Previous experience in leading cross-functional teams and collaborating with stakeholders across different departments.
• Excellent communication and presentation skills, with the ability to translate complex data analysis into clear and concise business implications.
• Experience with machine learning techniques and advanced analytics (e.g., clustering, regression, decision trees) applied to banking data, is a plus.