• Local Demand Planning and Forecasting Process at Cluster level• Provide key internal stakeholders with relevant and accurate analysis and insights. Recommend forecast adjustments based on market expertise, trend analysis and different forecasting approaches. • Reduced turn around time of total workflow process from 25hrs to 10hrs. This reduction in time allowed for improvement and analysis into various other projects. With a change in process to Splits current turn-over rate is 30 mins. Which includes checks to ensure workflow is correct. Workflow has been continuously improved during time in position.• Overhauled Papers forecasting system, splitting up organic trends with price activity volumes overlaid. Correlate Papers and FCT relationship.• Reporting to key stake holders on market status.o Updated Monthly Market Reports.o Including share and market size trends and forecast to global teamo Improved workflow of the process, the process shortened to ~1hr from the 1-2 days prior to improvement.o Stock Coverage reporting from various sources to stakeholders within the companyo Business Partnering Meetings to ensure Category Forecast was on track, future trends and insights were built into forecast model to ensure accuracy of full year targeto Deliver SOP Splits to all relevant stakeholders in timely manner.• Forecasting NPDs generated by the brands team• PowerBI Reporting created for analysis on various important stakeholder meetings. • Analysis of correlation between various internal categories for better and more accurate forecasting.• Forecasting and Machine Learning of Market Size based on Customer Scan Rates and Prices.o Using statistical analysis and various models such as SARIMAX, ARIMA, CES, ETS

• Started the career as an intern and was promoted to the position of a Junior Data Scientist – Technical Lead, reporting to the Technical Director • Leveraging internal GIS software to prepare/review and process input GIS data to the final solutions o Contributed throughout the entire data life cycle for the process o Creating, updating, and maintaining geospatial data o Verifying data quality and validity o Assisting in developing data standards o Data management o Analysis and modeling• Served on the Urbanfinity project: o Assisting the development of leading IP at the moment known as "Context Map" o The project involves the usage of QGIS and at later stages Python o Performing visual programming within QGIS and developing automation for the product o The product involved the use of GeoPandas, Pandas libraries and Matplotlib• Performed data analysis of Geospatial Data files(GPKG, SHP, CSV) via Python and QGIS o Worked on a project which was preliminarily tree detection run using QGIS and GDAL on Raster Images via Watershed Segmentation o Handling conversion between CRS systems, MGA94 and WGS84 o Downloaded Data Set from ClipnShip, ELVIS Spatial and raster visualisation and calculations o Utilise internal tools and best practices for data management and data workflows o Assured the compatibility of data set with various CAD Platforms o Developed internal algorithms and models that was the precursor for full-automation of product generation

• Trained with structural practices within the engineering design cycle in the workplace environment• Learned to coordinate structural modelling with multidiscipline project teams • Gained good understanding of drafting (CAD) application