Leverage credit bureau data and other pertinent data sources to formulate tailored solutions for financial institutions, including both banks and non-banks, pertaining to retail and small to medium-sized enterprise (SME) loan portfolios.
Manage and manipulate extensive datasets using programming languages such as SAS, Python, and R.
Assist in the creation and implementation of advanced analytics models focused on risk management and credit scoring, utilizing tools like SAS or similar software.
Foster collaboration across multi-functional teams to deliver actionable insights and recommendations grounded in data analysis.
Continuously oversee and enhance data quality to uphold precision and comprehensiveness.
Apply statistical methods and data visualization techniques to convey information in a clear and compelling manner.
Stay up to date of the latest developments within the banking and credit risk management sector.
Requirements
The candidate should possess extensive experience in managing and manipulating substantial datasets.
Candidates are expected to bring a solid background in banking and credit risk, particularly within the retail and SME banking sectors.
The role necessitates a strong grasp of statistical methods and data visualization techniques, along with the capacity to convey complex data concepts effectively to non-technical stakeholders.
At least bachelors or masters degree in a relevant field (e.g., statistics, mathematics, computer science, etc.) is required.
A minimum of 4 years of analytics experience is necessary, with a primary focus on credit risk management and/or banking.
Proficiency in data and modeling tools like SAS, Python, and R is essential.
Demonstrated expertise in handling large datasets and databases, advanced data manipulation skills, and proficiency in data manipulation using tools such as SAS, R, and Python, including experience with data visualization tools.
Exceptional problem-solving and analytical capabilities are a must.
Strong communication and teamwork skills are essential for success in this role.
A deep understanding of retail and SME banking, coupled with a background in credit risk, is preferred. Experience with credit bureau data is a valuable asset.
Proven ability to work both independently and collaboratively within a team is highly regarded.
