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Key Responsibilities and Required Skills for Credit Risk Analyst

💰 $60,000 - $120,000

Credit RiskFinanceAnalyticsRisk Management

🎯 Role Definition

This role requires an experienced Credit Risk Analyst to join our risk management team to assess, model, monitor, and report on credit exposures across consumer, retail, and corporate loan portfolios. The ideal candidate will combine strong credit judgment with analytical and technical capabilities (credit scoring, PD/LGD/EAD modeling, IFRS 9 / CECL provisioning, stress testing and portfolio analytics) to support underwriting, portfolio management, regulatory reporting, and strategic decision making. This role requires fluency in data-driven risk analysis, experience with statistical tools (SAS, R, Python), SQL and visualization platforms, and the ability to translate quantitative results into clear business recommendations for senior stakeholders.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Credit Analyst / Loans Operations Analyst
  • Risk Analytics Associate / Data Analyst (Finance)
  • Underwriting Analyst / Portfolio Analyst

Advancement To:

  • Senior Credit Risk Analyst
  • Risk Modeler / Credit Risk Model Validation Lead
  • Credit Risk Manager / Portfolio Risk Manager

Lateral Moves:

  • Credit Underwriting or Credit Policy Specialist
  • Regulatory Reporting or Compliance Analyst
  • Commercial Banking Relationship Manager with risk focus

Core Responsibilities

Primary Functions

  • Lead end-to-end credit risk assessments for loan origination and portfolio reviews, synthesizing borrower financials, qualitative credit factors, collateral valuation and industry trends into clear, documented credit recommendations that support underwriting and portfolio actions.
  • Develop, implement and maintain credit scoring models and predictive analytics (PD, LGD, EAD) using statistical and machine learning techniques; ensure models are production-ready, documented and aligned with governance standards.
  • Perform ongoing model validation, back-testing and performance monitoring to measure accuracy, stability and discrimination; produce validation reports and remediation plans in line with internal model risk policy and regulatory expectations.
  • Calculate expected credit loss (ECL) provisions and lifetime loss estimates under IFRS 9 and CECL frameworks; prepare macroeconomic scenario overlays, staging assessments and reconciliations for finance and accounting.
  • Conduct stress testing and scenario analysis on portfolios and strategic exposures to quantify downside credit risk under adverse economic conditions; present results to senior management and embed findings into risk appetite and capital planning discussions.
  • Monitor portfolio quality and credit metrics (delinquency, charge-off, vintage, roll rates, concentration risk) and prepare weekly/monthly management reports and dashboards for business partners and executive committees.
  • Create and maintain automated ETL pipelines and data models (SQL, Python) to aggregate borrower-level and exposure-level data from core banking systems and third-party sources; ensure data quality, lineage and reconciliation.
  • Partner with business stakeholders and underwriting teams to design and optimize credit policies, approval matrices, covenant structures and credit limit frameworks that balance growth and risk appetite.
  • Conduct counterparty and sectoral credit risk analysis for corporate borrowers, assessing financial statements, cash flows, covenants, collateral, and industry risk drivers; issue risk ratings and recommended limits.
  • Support loan origination by performing borrower creditworthiness checks, KYC/AML screening, covenant drafting suggestions, and advising on documentation to reduce credit risk and accelerate approvals.
  • Build and maintain interactive dashboards and visualizations (Tableau, Power BI) that translate complex credit analytics into actionable insights for credit committees, business leaders and portfolio managers.
  • Lead credit model implementation projects, working with IT, data engineering and production teams to deploy models into decisioning systems, monitor production performance and resolve model calibration issues.
  • Execute portfolio segmentation and cohort analysis to identify early warning signals and emerging sub-portfolios at risk; recommend targeted remediation strategies including pricing, restructuring or enhanced monitoring.
  • Prepare regulatory submissions and support regulatory exams related to credit risk (Basel III/IV, IFRS 9, CECL, CCAR where applicable), coordinating with regulatory reporting, finance and audit functions.
  • Conduct deep-dive loss event analysis and root cause investigations for significant default clusters; document findings and develop short- and long-term remediation and mitigation plans.
  • Support credit risk limit setting, concentration management and collateral optimization; quantify potential loss given default under varying recovery assumptions and legal/jurisdictional constraints.
  • Provide analytical support for credit pricing decisions, embedding risk-adjusted return metrics and expected loss calibrations into loan pricing models and product profitability analyses.
  • Maintain and update credit policy documentation, rating scales, model documentation, and risk playbooks; ensure all artifacts are audit-ready and reflect current methodologies and controls.
  • Facilitate quantitative credit training sessions and knowledge transfer for underwriting teams and junior analysts to raise analytical standards across the organization.
  • Coordinate and support internal and external audits of credit loss processes, model governance and data controls; implement remediation actions and test results to strengthen control framework.
  • Analyze new product proposals and strategic initiatives from a credit risk perspective, performing pilot monitoring and rolling out risk mitigants for early-stage products or new markets.
  • Collaborate with collections and recovery teams to design segmentation strategies for delinquent accounts, optimizing workout approaches and maximizing recoveries while minimizing operational cost.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis for business partners and executive leadership.
  • Contribute to the organization's data strategy and roadmap by identifying key upstream data quality and instrumentation gaps that impact credit measurement.
  • Collaborate with business units to translate data needs into engineering requirements for model input feeds and reporting systems.
  • Participate in sprint planning and agile ceremonies within the data engineering team to prioritize credit analytics deliverables.
  • Maintain comprehensive documentation and change logs for model updates, policy changes, and dataset refreshes to ensure transparency and traceability.
  • Assist in vendor assessments and third-party model reviews for credit bureau, alternative data, model providers and collections platforms.
  • Provide mentorship and coaching to junior analysts; review peer work products to ensure analytical rigor and adherence to methodology.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced credit risk modeling: development and validation of PD, LGD and EAD models; lifetime expected loss estimation and segmentation.
  • IFRS 9 and CECL provisioning expertise: staging criteria, macroeconomic scenario design, impairment methodology and reconciliations with finance.
  • Strong statistical and machine learning proficiency: regression, survival analysis, tree-based models (XGBoost, Random Forest), and model evaluation metrics.
  • Programming and data tools: Python (pandas, scikit-learn), R, SQL for data extraction, transformation, and model implementation.
  • Experience with analytical/statistical packages: SAS, Stata, or similar, and ability to operationalize code into production environments.
  • Advanced Excel skills including pivot tables, macros (VBA) and complex financial modeling.
  • Data visualization and reporting tools: Tableau, Power BI or equivalent to build management dashboards and automated reports.
  • Familiarity with credit decisioning and risk systems (e.g., Moody's Analytics, FICO, Experian, Oracle/Finacle, SAS Risk) and integration best practices.
  • Knowledge of regulatory frameworks and capital requirements: Basel III/IV, local banking regulations, and best practices for governance and model risk management.
  • Experience with stress testing frameworks, scenario analysis, reverse stress tests and capital planning inputs.
  • SQL-based ETL and data pipeline understanding; ability to discuss data lineage, data quality controls and reconciliation processes.
  • Understanding of loan products and structures: unsecured consumer loans, mortgages, trade finance, commercial lending and syndicated facilities.
  • Exposure to collections analytics and recovery strategies including segmentation and propensity-to-pay models.
  • Familiarity with cloud analytics platforms and version control systems (Git) is a plus.

Soft Skills

  • Strong analytical reasoning with ability to synthesize quantitative results into concise business recommendations.
  • Excellent written and verbal communication skills; experience presenting technical findings to non-technical senior stakeholders and credit committees.
  • High attention to detail and commitment to documentation, controls and audit readiness.
  • Effective stakeholder management and collaboration across product, finance, legal, IT and collections teams.
  • Problem-solving orientation with drive to identify root causes and implement pragmatic mitigations.
  • Time management and prioritization skills to balance regulatory deadlines, model releases and business-as-usual reporting.
  • Commercial mindset with the ability to balance risk mitigation and revenue objectives.
  • Resilience and adaptability in a fast-paced environment; comfortable with ambiguity and evolving regulatory requirements.
  • Ethical judgment and professional integrity when dealing with sensitive borrower and portfolio data.
  • Coaching and mentorship capabilities to uplift junior team members and standardize best practices.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Finance, Economics, Statistics, Mathematics, Data Science, Accounting, Engineering, or a related quantitative discipline.

Preferred Education:

  • Master’s degree in Finance, Financial Engineering, Applied Statistics, Data Science, or MBA.
  • Professional certifications such as CFA, FRM, PRM, or certifications in credit risk analytics/modeling are advantageous.

Relevant Fields of Study:

  • Finance
  • Economics
  • Statistics / Applied Mathematics
  • Data Science / Computer Science
  • Accounting
  • Financial Engineering

Experience Requirements

Typical Experience Range:

  • 2 to 6 years of progressive experience in credit risk, credit analytics, underwriting, risk modeling, or portfolio management.

Preferred:

  • 3+ years of hands-on experience building and validating credit models (PD/LGD/EAD) and applying IFRS 9 / CECL provisioning.
  • Prior experience in a banking, lending, consumer finance, or credit bureau environment; exposure to regulatory reporting and audit processes highly preferred.
  • Demonstrated track record of delivering production-ready analytics, automating reporting, and influencing credit policy or underwriting practices.
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