Job Description: We are seeking a Risk Analytics / Loss Forecasting Analyst with strong Python, Excel, and SQL skills to support the automation and modernization of consumer credit loss forecasting processes . The role focuses on transitioning existing Excel and Excel macro–based forecasting models into scalable, auditable SQL and Python script–based workflows , while ensuring accuracy, governance, and business continuity.
Key Responsibilities
- Analyze existing Excel and VBA-based loss forecasting models and document underlying business logic and assumptions
- Translate Excel formulas, macros, and manual processes into Python-based scripts and workflows
- Develop automated pipelines for data ingestion, transformation, and forecast generation
- Perform loss forecasting, back-testing, and sensitivity analysis on consumer credit portfolios
- Use SQL to extract, validate, and reconcile data from databases and data warehouses
- Ensure Python outputs reconcile with legacy Excel forecasts and meet defined tolerance thresholds
- Build reusable, modular, and well-documented Python code for ongoing production use
- Support scenario analysis and stress testing through parameterized Python models
- Collaborate with risk, finance, and business stakeholders to validate assumptions and outputs
- Maintain version control, documentation, and audit trails for models and forecasts
Required Skills & Qualifications
- 2–4 years of experience in banking analytics, loss forecasting, or credit risk modeling
- Strong proficiency in Python (pandas, NumPy, basic modeling/statistical libraries)
- Advanced Excel skills, including experience with complex formulas and macros (VBA)
- Solid SQL skills for querying and validating large datasets
- Hands-on experience with loss forecasting methodologies (roll rates, vintage analysis, PD/LGD, or loss rate forecasting)
- Strong analytical skills with attention to detail and data quality
Preferred / Nice-to-Have Skills
- Experience working in banking, credit cards, unsecured lending, or BNPL portfolios
- Familiarity with automation frameworks, schedulers, or workflow tools
- Experience validating or migrating legacy models to modern analytics platforms
