Intersectionality reveals that women of color in data science face compounded wage disparities due to overlapping identities related to race and gender. Historical exclusion from STEM fields means these women often start with lower salaries compared to their white and male counterparts. This systemic background contributes to persistent compensation gaps that are difficult to close without targeted interventions.
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