As part of the commodity pricing team for B2C, I bridge data analytics with operational insights to shape strategic decisions in a dynamic energy market. Combining my knowledge of our sourcing processes and operational activities, I support pricing and sourcing strategies for B2C customers through recommendations to our senior management team.Key responsibilities include:- Supporting the integration of new software solutions and optimizing data usage for the pricing team.- Developing and analyzing electricity and gas pricing models that adapt to evolving laws and energy market trends, including solutions for solar panel customers.- Utilizing Python, SQL, and company-specific tools to enhance data-driven decision-making.- Supporting the energy transition by ensuring our pricing models align with market developments and customer needs.

Took lead on developing a machine learning approach to predictive maintenance for DSLAM cabinets across the country. Multiple machine learning methods (Logistic Regression, Decision Trees, Random Forests, and Neural Networks) were applied and researched by me. The data used in the project was stored on a non-sql big data database (Hadoop) and I utilized PySpark to work on this data.Furthermore, research was done regarding the operational processes and complexities of implementing such a model. I also wrote my master thesis on this topic while doing this internship.

I have been a teaching assistant for the course: "Skills-it" for IBA year 1 at RSM where I taught classes of around 40 people. This course focuses on Business Modelling and Analytics using Excel. This course goes beyond the basics of Excel and teaches advanced functions and tools such as Solver to be well prepared for the working environment.