Brandon Rufino
Director Of AI For Clinical Trial Design at Sanofi
Toronto, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolBrandon Rufino is a Director of AI for Clinical Trial Design based in Toronto with eight years of experience applying advanced data science to drug development. He leads cross-disciplinary teams at Sanofi to build full-stack AI pipelines that blend transformer architectures, predictive modeling, and causal inference to optimize trial parameters, patient safety, and success probability. Comfortable both as a hands-on coder and a strategic manager, he has driven MLOps standards, reusable component architectures, and organization-wide upskilling programs. His background spans biomedical engineering, EHR and literature-driven patient journey modeling, and even mixed-signal layout work—an uncommon blend that helps him bridge computational, clinical, and hardware-minded perspectives. Brandon thrives on collaborative partnerships with academia and industry to push practical AI frontiers that shorten development time for therapeutics.
8 years of coding experience
4 years of employment as a software developer
Electrical and Biomedical Engineering, Alumni, Electrical and Biomedical Engineering, Alumni at McMaster UniversityMaster of Applied Science (MASc), Biomedical Engineering, Master of Applied Science (MASc), Biomedical Engineering at University of TorontoHighschool Diploma with a Specialist High Skills Major (SHSM) in Sports, Highschool Diploma with a Specialist High Skills Major (SHSM) in Sports at St. Edmund Campion High School
English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (13)
sports-data8
visualize7
espn7
google-analytics5
react5
api5
audio4
yahoo4
derive3
bert3
css1
javascript1
nextjs1
Programming languages (2)
Github contributions (5)
brufino/practical-nlp
Apr 2021 - May 2021
Contributions:2 pushes in 1 month
Sanofi-Public/Clinical-BERT-Explainability
Jan 2025 - Jul 2025
To explain clinical BERT model predictions, we present an approach which leverages integrated gradients to attribute events in medical records that lead to an outcome prediction.
Contributions:1 push in 6 months
bertbert-fine-tuningclinical-dataexplainable-aiintegrated-gradients
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