My name is Tigist Ayele, and I'm currently a Data Science MSc student at the University of Essex with a background in civil engineering and project management.

Over the past few years, I’ve transitioned from infrastructure development into the world of data science—driven by a desire to apply analytical tools to create meaningful, socially beneficial impact.

Since making this transition, I’ve developed hands-on experience in machine learning, data engineering, and predictive modeling. Highlights of my work include:

Building a deep neural network to forecast sales across Rossmann stores in Germany (Kaggle project).

Conducting a COVID-19 trend analysis using R, identifying peak patterns and visualizing county/state-level variations across the US.

Leading multiple end-to-end data projects through DataCamp and WorldQuant University, from analyzing crime in Los Angeles to exploring housing markets in Latin America and investigating air pollution in Nairobi.

My technical toolkit includes Python, R, SQL, TensorFlow, Scikit-learn, and Pandas, with strong skills in data wrangling, visualization, and modeling.

As a volunteer, I’m particularly drawn to projects that intersect with public health, environmental sustainability, education, or urban development—areas where data insights can directly improve lives. I would be excited to join scoping calls, contribute to modeling, or help translate technical findings into actionable insights for stakeholders.

Ultimately, my goal is to both give back and grow by supporting mission-driven initiatives and continuing to hone my ability to apply data science for good. I believe in the Solve for Good model and would be honored to be part of a global network of volunteers striving to make a difference.