Summary
Daniel Simeone is a data scientist with 12 years of experience applying statistical methods and data-driven policy analysis across government and consulting roles in Canada. Trained in mathematics and statistics (MSc, BAH) with advanced humanities research (MA/PhD studies at McGill), he blends rigorous quantitative skills with deep contextual thinking to translate complex evidence into actionable public policy. He has led analytics and knowledge mobilization projects for Manitoba Education and now drives applied data science at Purpose Analytics, focusing on making data usable for decision-makers. A seasoned educator who has taught at university and college levels, he brings clear communication and a knack for explaining technical findings to diverse audiences. Uncommonly for a practitioner in policy analytics, his background in classical studies and theatre adds a narrative sensitivity that informs how he frames data stories.
12 years of coding experience