Residual Value Forecasting Analyst

Volkswagen Financial Services; Milano; In loco; EUR 10.000 - 12.000; Tempo pieno

Descrizione del lavoro

Volkswagen Financial Services Milano seeks a motivated data analyst to support RV forecasting models and data preparation for residual forecasting initiatives. You will collaborate with Sales, Marketing, Brand Management and Remarketing to ensure accurate RV management and reporting for budgeting and planning.

The role requires strong quantitative background, programming in Python/SAS, advanced Excel, and the ability to handle large datasets with precision and speed in a fast-paced automotive

Competenze

• Degree in Economics, Computer Science, Engineering, Mathematics, Statistics, or another quantitative field.
• Strong knowledge of financial mathematics and statistical methods.
• Experience with data mining, machine learning and modeling.
• Proficiency in Excel and PowerPoint for reporting and presentation.
• Programming experience in Python and SAS (SAS Viya).

Mansioni

• Support maintenance of models for RV forecast.
• Prepare datasets to support residual forecasting approaches.
• Collaborate with Sales, Marketing, Brand Management and Remarketing on RV management.
• Analyze remarketing data performance.
• Prepare data for RV committees at local and HQ levels.
• Assist with impairment calculations and RV cost reporting for budgeting and forecasting.
• Carry out ad hoc analyses as needed.

Conoscenze

Financial mathematics

Statistics

Data Mining

Machine learning

Excel

PowerPoint

Python

SAS

SAS Viya

Formazione

Degree in Economics or quantitative field

Strumenti

Python

SAS

SAS Viya

Excel

PowerPoint

Descrizione del lavoro

Volkswagen Financial Services Milano seeks a motivated data analyst to support RV forecasting models and data preparation for residual forecasting initiatives. You will collaborate with Sales, Marketing, Brand Management and Remarketing to ensure accurate RV management and reporting for budgeting and planning.

The role requires strong quantitative background, programming in Python/SAS, advanced Excel, and the ability to handle large datasets with precision and speed in a fast-paced automotive