Camila Andrea Gonzalez Williamson

Implementation of industrialization and automation of a data pipeline to analyze specific global trends.

This was a multidisciplinary team effort that involved the collection of external data sources, an extensive work of data wrangling and text manipulation, the use of data science techniques such as hierarchical clustering, regression, and statistical inference, and the exposure of the results via a dashboard accessible as a web application.

The estimated business value for this data product was in the order of tens of millions of dollars.

A data-processing-pipeline to cluster adoption patterns to a specific line of products using distributed computing.

This was a team effort that involved the analysis of behavioral patterns in multi-channel customer data to identify actionable opportunities for improvement in the consumer journey. During the development, we integrated data from different sources, verified the data integrity, processed the data with Python and Spark (outlier treatment, filtering, aggregation, feature generation), generated insights from clustering and conversion models, and exposed the final results in a dashboard.

This project was deployed in 13 markets and brought tangible improvements to key performance indicators (KPIs) with estimated business value in the order of millions of dollars.

A short-term forecast for the Swiss electric load for the day-ahead or intraday market.

I was the main person in charge of implementing and evaluating a novel machine learning technique for short-term load prediction used by the Swiss electric grid operator. The resulting model successfully incorporated seasonal patterns at the yearly, weekly, and daily levels and non-linear dependencies with the temperature.