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
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
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
