Zain Nasrullah
Senior Director at RBC
Canada
Summary
🤩
Rockstar🎓
Top SchoolZain Nasrullah is a senior AI validation leader with nine years of experience building and operationalizing robust, responsible machine learning programs at scale, currently serving as Senior Director at RBC. He leads teams that "break models" to improve them—designing testing frameworks and research on robustness, uncertainty, fairness, and transparency across diverse model classes and business units. Previously at PwC and GE Digital, he combined consulting, product analytics and infrastructure work with academic research, publishing in IJCNN, JMLR and SIAM. An active open-source contributor, he implemented and refactored the LSCP ensemble in the widely used PyOD anomaly detection library, demonstrating deep algorithmic and production-grade coding skills. He frequently speaks on AI governance and mentors practitioners on ML, analytics and automation, bridging rigorous research with pragmatic, auditable controls. Trained with an MScAC from University of Toronto, he uniquely blends engineering, research and governance to scale trustworthy AI in regulated environments.
9 years of coding experience
7 years of employment as a software developer
High School, High School at John Fraser Secondary SchoolMaster of Science in Applied Computing (MScAC) Computer Science, Master of Science in Applied Computing (MScAC) Computer Science at University of TorontoHigh School, High School at Dubai American Academy
English, Urdu
Github Skills (8)
machine-learning10
anomaly-detection10
python10
outlier-detection10
scikit-learn9
scikit9
unsupervised-learning8
data-science8
Programming languages (1)
Github contributions (5)
yzhao062/pyod
Nov 2018 - May 2020
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
Role in this project:
ML Engineer
ML EngineerContributions:44 commits, 4 PRs, 22 pushes in 1 year 6 months
Contributions summary:Zain implemented and refactored the LSCP (Locally Selective Combination of Parallel Outlier Ensembles) model within the pyod library. Their contributions included refactoring code, fixing bugs related to local region size and feature selection, and modifying the model to use the decision function for outlier scoring. The user also integrated the model into the example code, demonstrating their understanding of the algorithm and its implementation.
pythondata-miningoutlierspython2outlier-ensembles
ZainNasrullah/computer-vision-deep-learning-intro-papers
Jan 2018 - Oct 2018
Contributions:19 commits, 17 pushes, 1 branch in 8 months
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