Lindsay Coome
Principal UX Researcher at SAP
Old Toronto, Ontario, Canada
Summary
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Senior🎓
Top SchoolLindsay Coome is a Principal UX Researcher with a decade of experience blending experimental psychology, neuroscience, and data science to inform product strategy and design. With a PhD from the University of Toronto and roles spanning academia and industry, she leads end-to-end user research at SAP, translating rigorous experimental methods into actionable insights for large-scale enterprise software. Her work is both prolific and influential—27 peer-reviewed papers, numerous conference presentations and chapters, and media citations from outlets like The New York Times and The Wall Street Journal—reflecting an ability to surface findings that resonate beyond academia. She has moved between hands-on data science and UX leadership roles at organizations including ZipRecruiter, Hootsuite, and Surgo Health, demonstrating fluency in research design, analytics, and cross-functional collaboration. Known for combining scientific rigor with product pragmatism, she often applies advanced experimental and statistical techniques to uncover subtle user behaviors that drive measurable product improvements.
10 years of coding experience
5 years of employment as a software developer
Honours Bachelor of Arts (B.A.) Psychology, Honours Bachelor of Arts (B.A.) Psychology at Western UniversityDoctor of Philosophy (Ph.D.) Experimental Psychology and Neuroscience, Doctor of Philosophy (Ph.D.) Experimental Psychology and Neuroscience at University of TorontoInstitute for Management and Innovation Certificate in Data Science and Analytics, Institute for Management and Innovation Certificate in Data Science and Analytics at University of Toronto MississaugaGerman Neuroinformatics Node Advanced Scientific Programming in Python Summer School, German Neuroinformatics Node Advanced Scientific Programming in Python Summer School at University of Camerino
Github Skills (27)
coders10
spss9
quantitative8
stable8
knitr8
r8
data-wrangling7
treasury7
statistics7
data-visualisation7
bayesian7
machine-learning6
python6
corona6
mass6
Programming languages (6)
Github contributions (5)
lcoome/rcourse
Oct 2017 - Sep 2018
Reproducible Quantitative Methods for EEB
Contributions:4 PRs, 13 pushes, 5 branches in 11 months
pythonmachine-learningreproduciblequantitative-methodsquantitative
lcoome/studyGroup
Feb 2017 - Feb 2017
Contributions:36 pushes in 1 day
welcomeuniversity-of-torontocodersscientific
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