Lightcast provides gender insights derived from profile data, enabling analysis across dimensions such as region, occupation, company, school, and skills. This approach supports granular segmentation for benchmarking and exploration.
Because profile data availability varies across countries and occupations, coverage may differ depending on the population being analyzed.
Gender is inferred using a model based primarily on first names found within profiles. The model is trained using profiles that include self-reported gender, along with publicly available name-to-gender datasets derived from native speaker usage. Using these inputs, the model assigns a likely gender where sufficient confidence exists.
To maintain data quality, the model avoids making assignments in ambiguous cases. It does not account for regional variations in name usage (for example, names that may be associated with different genders across countries). In such cases, the gender is categorized as "Unknown."