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

Sumitomo Mitsui Banking Corporation

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Posted 1 day ago

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.