Fires affect ecosystems, global vegetation distribution, atmospheric composition, and human-built infrastructure. The climatic, socio-economic, and environmental factors, which affect global fire activity, are not well understood, and thus their contribution is parameterized in global process-based vegetation models. Fire's climatic and ecological characteristics have been successfully identified using data-driven modeling approaches such as machine learning models; however, socio-economic factors at a global scale have not been explored in detail. Humans alter fire activity by different means, e.g., by acting as a source of ignition, fire suppression, and changing fuel availability and structure. These factors cannot easily be integrated into process-based vegetation models. Data-driven models can thus characterize these factors in time and space, enabling their better representation in process-based models. We created an ensemble of random forest models to test several socio-economic variables' importance in predicting fire ignition occurrences on a global scale, starting with a baseline model characterizing climate and vegetation, then training subsequent interactions with a single socio-economic variable (e.g., population density, Gross Domestic Product, and Distance to population centers). Our models successfully capture the seasonality and spatial distribution of fire hotspots. High ignition occurrence across Sub-Saharan Africa positively influences the models' ability to predict fires in regions with seasonal ignition occurrence. The models, in general, reduce bias in ignition predictions compared to observations when a socio-economic variable known to influence fire ignitions is added to the base model. Our models also demonstrate the importance of specific variables in reducing bias in annual ignition sums between the baseline model predictions and observations, e.g., over Sierra Leone and most of Kenya, population and livestock density reduce bias in annual ignition sums. We also show the power of our models to reproduce fire occurrence seasonality, even over regions where observations of fire ignitions are rare. Finally, we discuss how using data-driven modeling and multiple socio-economic variables can help inform the development of process-based vegetation models.