Women are grievously underrepresented in the mathematical sciences. Because publication of research is key to academic career advancement and because research has repeatedly uncovered gender bias penalizing women in professional circumstances, we use tools of data science to study 600 mathematical sciences journal editorial boards. We quantify gender representation on these boards and examine its association with characteristics such as impact factor, publishing house, and mathematical subfield.