Scored Lending Analyst at Standard Bank Group in Nairobi, Kenya

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    Scored Lending Analyst

    Standard Bank GroupNairobi, Kenya

    Posted

    6 days ago

    Apply by

    31 Aug

    Full Time
    On Site
    Senior
    Banking, Insurance & Financial Services
    Data, Analytics & AI

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    Job Description

    Lead and oversee the Scored Lending portfolio by managing credit risk, portfolio performance, and policy adherence within the Bank's risk appetite. Prepare and deliver high-quality credit risk and portfolio reporting to management and governance committees. Drive data-driven decision-making through continuous monitoring of portfolio trends, risk indicators, and emerging risks to support sustainable growth.

    Key Responsibilities

    • Lead and oversee the Scored Lending portfolio by managing credit risk, portfolio performance, and policy adherence within the Bank's risk appetite.
    • Prepare and deliver high-quality credit risk and portfolio reporting to management and governance committees.
    • Drive data-driven decision-making through continuous monitoring of portfolio trends, risk indicators, and emerging risks to support sustainable growth.

    Required Qualifications

    • First Degree Field of Study: Statistics, Mathematics, Actuarial Science, Economics, Finance, Data Science, Banking, Accounting, Computer Science, or a related quantitative discipline.
    • Experience Required 5-7 years Credit Data Analytics within consumer banking.
    • Experience in preferably Digital consumer credit data exploitation and business intelligence development and implementation.
    • Experience in the extraction, transformation and visualisation of data using bank approved toolsets e.g. SQL/SAS/Python/PowerBi.
    • Articulating Information
    • Documenting Facts
    • Examining Information
    • Exploring Possibilities
    • Interpreting Data
    • Managing Tasks
    • Providing Insights
    • Team Working
    • Data Management
    • Evaluating Risk Management Effectiveness
    • Risk Awareness
    • Risk Management
    • Risk Reporting
    • Statistical & Mathematical Analysis

    Job Details

    Job Function

    Data, Analytics & AI

    Minimum Experience

    5 years

    Education Level

    Bachelor’s Degree

    Area of Study

    Science

    Field of Study

    Statistics

    Languages

    English

    Additional Information

    How to Apply: Visit the application link provided on this page to submit your application.

    Show Your Credit Risk Analytics Chops

    This role sits at the intersection of data science and credit risk. Hiring managers will be looking for proof that you can turn raw data into decisions that protect the bank's portfolio while enabling growth.

    1. Quantify your impact: On your CV, show numbers that matter: portfolio size, risk reduction percentages, or how your dashboards changed decision-making. For example, 'Reduced default rates by 15% through early warning models.'

    2. Master the tools: Be ready to discuss your hands-on experience with SQL, SAS, Python, and PowerBi. Prepare a short walkthrough of a complex data extraction or visualization you built.

    3. Understand the consumer lending lifecycle: Show you know how credit data flows from application to collections. Mention specific stages like origination, monitoring, and collections.

    4. Speak risk fluently: Expect questions on risk metrics like PD, LGD, ECL, and how you've used them. Be ready to explain how you've monitored risk indicators and reported to committees.

    5. Prepare a portfolio case study: Have a story ready about a time you spotted a risk trend and acted on it. Walk through the data, the analysis, and the outcome.

    6. Show business acumen: This isn't just a technical role. Talk about how you've balanced risk with growth, and how you've communicated insights to non-technical stakeholders.

    7. Know the regulatory landscape: Familiarize yourself with Kenyan banking regulations like CBK guidelines on credit risk. Mentioning these shows you're ready for the local context.

    8. Practice your presentation: You'll likely have to present your findings. Prepare a clear, concise deck on a sample analysis, and practice explaining it without jargon.

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    Tags

    credit risk
    data analytics
    SQL
    SAS
    Python