B1_Automation

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

• 0–3 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

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