Background
As Senior Data Scientist II at Teradata, I worked on sales forecasting for IKEA across more than 30 countries, from Sweden to Japan.
My approach
I built a forecasting pipeline that combined time series algorithms with local market understanding. The system accounted for everything from national holidays to local weather conditions.
Technologies
- Python & R for statistical modeling
- Spark for distributed data processing
- Teradata for data warehousing
Results
The forecasts were built for inventory and supply-chain planning across countries with very different local conditions.
The work taught me how quickly a technically strong model becomes an organizational question about data, planning and accountable use.