Brian Dalessandro
Director Data Science, AI Solutions Automation
City of New Rochelle, New York, United States
Summary
👤
Senior🎓
Top SchoolBrian Dalessandro is a data science executive and educator with 11 years of industry experience leading ML and AI programs across Meta, Instagram, Capital One, and startups. He builds and scales predictive systems that drive integrity, support, well-being, and social impact, while also creating platforms to democratize modeling for business users. As an adjunct professor at NYU Stern he translates academic rigor into practical courseware—his public DataScienceCourse repo contains notebooks that mirror the hands-on curriculum he teaches. Known for pairing research-grade causal and experimental methods with product-focused delivery, he has led teams from R&D to enterprise optimization and automated model-building systems. Based in New Rochelle, NY, he combines strategic leadership, mentorship, and an ongoing commitment to making data science accessible to diverse and marginalized communities.
11 years of coding experience
20 years of employment as a software developer
MBA Statistics, MBA Statistics at NYU Stern School of BusinessBA Mathematics, BA Mathematics at Rutgers University
French
Github Skills (16)
scikit10
pandas10
logistic-regression10
machine-learning10
python10
scikit-learn10
feature-engineering9
data-visualisation9
data-visualization9
data-visualizations9
decision-tree9
eval8
evaluation8
auc8
random-forest8
Programming languages (1)
Github contributions (5)
briandalessandro/DataScienceCourse
Oct 2014 - Dec 2020
This holds iPython notebooks and lecture slides for the Intro to Data Science Master's course I teach at NYU.
Role in this project:
Data Scientist
Data ScientistContributions:1 review, 211 commits, 31 PRs in 6 years 3 months
Contributions summary:Brian's contributions primarily involved the implementation of data science concepts within the Intro to Data Science Master's course repository. The user implemented a Pandas introduction to the use of DataFrames, illustrating key operations, and then proceeded to simulate a Bernoulli distribution and build and visualize Logistic Regression models using Scikit-learn. They also wrote code to analyze feature importances within that Logistic Regression model. Furthermore, the user explored resampling methods and applied several machine learning models including Logistic Regression, Random Forests, SVM, and K-Nearest Neighbors.
data-scienceipython
briandalessandro/explorations
Jan 2019 - Nov 2020
Contributions:11 commits, 10 pushes, 2 branches in 1 year 10 months
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