Jacob Nogas
Data Scientist
Old Toronto, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolJacob Nogas is a data scientist with nine years of experience applying machine learning and statistics to real-world problems, currently building experimentation and ML tooling at Lyft after recent work on advertiser-facing ML at Amazon. His background spans NLP robustness for legal tech, multi-armed bandit algorithms and A/B testing for online education, and deep learning for assistive technology, reflecting a rare mix of research-grade methods and production engineering (PySpark, Airflow, AWS). At Amazon he helped design experiments that informed ~$30M in annual revenue and automated metric pipelines that saved recurring analyst time, showing an ability to translate models into measurable business impact. Trained at the University of Toronto with research that improved bandit experiment power and reduced false positives, he brings both rigorous statistical thought and practical deployment experience. Colleagues know him for bridging product, research, and engineering to deliver interpretable, robust solutions in high-stakes domains.
9 years of coding experience
4 years of employment as a software developer
MSc Computer Science (PhD Track), MSc Computer Science (PhD Track) at University of TorontoBachelor of Arts - BA Philosophy, Bachelor of Arts - BA Philosophy at York UniversitySimon Initiative LearnLab Summer School, Simon Initiative LearnLab Summer School at Carnegie Mellon University
Github Skills (17)
pytorch10
python10
machine-learning10
autoencoder10
deep-learning10
adversarial-learning10
data-science9
tensorflow9
keras9
human-activity-recognition8
jax8
neural-network8
playing5
fft3
computer-vision3
Programming languages (1)
Github contributions (5)
JJN123/Image-Procesing
Apr 2017 - Mar 2019
Contributions:12 commits, 11 pushes, 1 branch in 1 year 11 months
techniquescomputer-visionconvolutionfftimage-processing
JJN123/Fall-Detection
Apr 2018 - Jan 2020
Non-invasive Fall Detection with Keras and Tensorflow
Contributions:55 commits, 96 pushes, 1 branch in 1 year 9 months
adversarial-learningautoencoderfalldeep-learninghuman-activity-recognition
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