Job Purpose Statement
The role is responsible for all aspects of portfolio management as relates to the quality of the loan book for all retail banking customer under Retail credit Solutions. The role holder is required to support in management and maintenance of a portfolio of customers within assigned market segments by analysing the portfolio to establish any trends and advise theRelationship management team, Branch teams and Work Place banking of any matters arising that may affect the portfolio. They shall offer full support to the team in all matters portfolio including but not limited to identifying growth gaps, early alert, portfolio at risk, non-performing loans, portfolio trends, market trends, salary/SOW compliance, scheme penetration, audit and compliance, covenant tracking. The roleholder will support assigned segment by providing all reports that will assist the team achieve its objectives.
Ideal Job Specifications
Academic:
Bachelor’s degree from an accredited university preferably Bachelor’s/Master’s degree inbusiness related field,Statistics,Mathematics,Computer Science Machine Learning, Artificial intelligence (or equivalent experience)
Desired work experience:
Extensive knowledge of statistical methods and data mining
3 years’ work experience in portfolio management with at least 1 year in a management positionwithin a retail-banking environment. 3+ years of experience building analytical data models / data sources and reports with the helpof data visualization software such as Looker, Tableau or Power BI, ideally in an agile and fast- growing start-up environment Great communicator with a sharp, analytical mind
Prior experience working with AI, ML, and Data Science
At least 3-5+ years of relevant experience as a Data Analyst, Data Science, and analytics tools Extensive experience with Tensorflow, Python, Tableau, and Big Data
Demonstrable experience applying data science methodologies to support business outcomes Extensive knowledge of statistical methods and data mining. Experience with general ML methods and expertise with Deep Learning, recommendationsystems, supervised learning and feature engineering, personalization, clustering, as well as time series and forecasting analysis In-depth knowledge of data science languages like Python, R, Scala, and SAS ML
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