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Manager, Credit Scoring at KCB Bank Kenya Job in Kenya Recruiting by JobWebKenya

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Manager, Credit Scoring at KCB Bank Kenya

🏢 JobWebKenya
📌 Kenya, Kenya, Kenya
💼 other general
⌚ 13 hours ago

🔖 Full Time

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Job Description

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Kenya Commercial Bank Limited is registered as a non-operating holding company which started operations as a licensed banking institution with effect from January 1, . The holding company oversees KCB Kenya – incorporated with effect from January 1, – and all KCB’s regional units in Uganda, Tanzania, Rwanda, Burundi, Ethiopia and South Sudan. It also owns KCB Insurance Agency, KCB Capital, KCB Foundation and all associate companies. The holding company was set up to among other things to enhance the Group’s capacity to access unrestricted capital and also enable investment in new ventures outside banking regulations, achieve operational and strategic autonomy for the Group’s operating entities and enhance corporate governance across the Group and oversight in management of subsidiaries.

Key Responsibilities

Design, development and maintenance of credit scoring models for use in core banking products as well as digital lending.

Full ownership of the model development process from conceptualization through data exploration, model selection, validation, implementation, and business user training and support.

Work closely with stakeholders to ensure adequate understanding of risk models and their application. Play a key role in the development of products that rely on credit scoring by providing analytics support in the design of product business rules and strategies.

Work with stakeholders throughout the organization to identify opportunities for leveraging data to drive business solutions using Advanced Analytics for the management of credit risk.

Development and validation of risk models for use in Loan Pricing, Provisioning, Stress Testing, ICAAP and other applications.

Understand, measure and manage model risk.

Assess the effectiveness and accuracy of new data sources, data governance activities (e.g. data quality and cleansing strategies) data gathering techniques and develop processes and tools to monitor, analyze and tune model performance and data accuracy.

Work with both structured and unstructured data including transforming of large, complex datasets into pragmatic and actionable insights.

Develop and maintain user and technical documentation/manuals on business requirements, data sources, ETL related activities, data quality assessment, data cleansing activities, data mining analyses, models developed, reports generated and statistical solutions developed and deployed.

Stay abreast of industry and regulatory trends that may impact new and existing strategy development.

The Person

For the above position, the successful applicant should have the following:

A bachelor’s degree in mathematics, Business, Statistics, Economics, Actuarial Science, Computer Science or equivalent combination of education and experience.

Proficient in SQL, R, Python, Supervised and Unsupervised Machine Learning Techniques.

At Least 5 years of proven performance in Data Science & Statistical Analysis.

Broad understanding of the credit risk management process with at least 3 years’ experience in credit/risk management

Experience in the use of Machine learning algorithms and techniques like supervised and unsupervised machine learning, clustering, neural networks, reinforcement learning, decision trees, regression, and adversarial learning.

Have extensive statistical analysis and/or data science experience utilizing R, Python and/or similar programming languages in manipulating data and drawing insights from large data sets.

Be an authority in querying and extracting large datasets from various sources for use in the development of credit scoring models and reporting.

Excellent team collaboration, verbal, written, and data presentation skills.

Flexible and capable of handling multiple tasks in a fast paced, high-volume environment.

Have an inquisitive nature with an aptitude to diagnose and tackle analytically complex business problems.

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