Partner with Wholesale Banking stakeholders to identify business opportunities, pain points, and growth levers across client coverage, lending, and risk management.
Leverage internal and external data sources to uncover trends, opportunities, and emerging risks.
Conduct deep-dive analyses and translate findings into actionable recommendations for business and management teams.
Develop analytical frameworks, dashboards, and visualizations that enable data-driven decision-making.
Drive end-to-end analytics use cases from problem definition and data exploration through implementation and impact measurement.
Work with large and complex datasets to generate insights that improve client acquisition, portfolio growth, and relationship management strategies.
Collaborate with data engineers, risk teams, and business stakeholders to improve data quality, accessibility, and usability.
Explore predictive analytics and machine learning approaches to enhance business outcomes and decision-making processes.
Support the development of analytical assets, models, and reusable frameworks that can be scaled across the Wholesale Banking franchise
Who you are
A degree in a quantitative field such as mathematics, statistics, or computer science
Possess overall 7-8 years work experience with at least 5 years of experience in data analytics, preferably in a sales or marketing context
Possessing banking domain knowledge specifically with strong familiarity in analytics for loans and credit risk is helpful
Possess strong analytical and problem-solving skills, with the ability to communicate complex data insights in a clear and compelling way
Experience with Python
Strong collaboration and communication skills, with the ability to work effectively with sales teams and other stakeholders
