Resume

Education

Columbia University

Master of Science in Data Science | New York, NY | Expected Dec 2026

Relevant Coursework: Applied Machine Learning, Algorithms for Data Science, Probability & Statistics, Exploratory Data Analysis & Visualization

Upcoming coursework: Statistical Inference & Modeling, Causal Inference for Data Science, Computer Systems for Data Science, Machine Learning for Data Science

La Salle University

Bachelor of Science in Mathematics | Philadelphia, PA | Aug 2018 - May 2022

Minors: Computer Science, Chemistry
GPA: 3.94 | Maxima Cum Laude
Honors: 2022 Outstanding Graduate of the Mathematics Department, Founder’s Scholarship

Professional Experience

Data Scientist II

Chubb Ltd | Philadelphia, PA | Jul 2022 - Jul 2025

Production ML Development

Built and deployed Generalized Linear Models for actuarial pricing decisions in Small Commercial insurance (Property, Auto, Worker’s Compensation)
Designed and implemented production-ready models impacting pricing decisions for thousands of policies

Data Engineering & Pipeline Optimization

Optimized data pipeline managing 10 external sources with automated extraction, preprocessing, and feature engineering
Improved model testing accuracy and computational efficiency through strategic pipeline redesign

Technical Leadership

Facilitated company-wide Python migration from legacy SAS codebase, converting ML pipeline for pricing models to ensure business continuity
Created comprehensive deployment documentation and governance frameworks for model auditing and refresh cycles

Cross-Functional Collaboration

Presented analytical findings to technical and non-technical stakeholders using PowerPoint, Excel, and QlikView dashboards
Mentored intern on NLP project for automated Cyber claims classification

Technical Environment: Python, SQL, Databricks, ADLS, Git, Jira, QlikView

Academic Projects

SkinWise: AI-Powered Skincare Recommendation System

Columbia University | Dec 2025

Developed sentiment classification system using TF-IDF and logistic regression on 581K Sephora reviews
Achieved 86% accuracy with 46% improvement in negative feedback detection (precision: 61% → 88%)
Designed review quality scoring algorithm to automatically select top-5 representative reviews per product
Validated multi-platform scalability by applying trained model to 4K unlabeled Ulta reviews

Tech Stack: Python, scikit-learn, pandas, NumPy

Bank Marketing Campaign Prediction

Columbia University | Nov 2025

Preprocessed 41K+ observations with 62 features from UCI Bank Marketing dataset
Developed and tuned ensemble models (Decision Tree, Random Forest, GBM, XGBoost) with GridSearchCV
Optimized Gradient Boosting achieving 36.9% F1-score and 46.4% AUCPR—22% improvement over baseline
Created comprehensive analysis including precision-recall curves, validation curves, and feature importance visualizations

Tech Stack: Python, XGBoost, scikit-learn, GridSearchCV

Technical Skills

Programming Languages
Python (pandas, NumPy, scikit-learn, H2O), SQL, R (ggplot2, tidyverse)

Machine Learning
Supervised/Unsupervised Learning, NLP, Sentiment Analysis, GLMs, Ensemble Methods (Random Forest, XGBoost), Feature Engineering, Model Evaluation

Data & Cloud Platforms
Databricks, Azure Data Lake Storage (ADLS)

Development Tools
Git/GitHub, Jira, Jupyter, VS Code

Data Visualization
Matplotlib, Seaborn, Altair, ggplot2, Excel, QlikView

Languages
English (Fluent), French (Native), Spanish (Conversational)

Interests

Research Interests: Recommendation Systems, Experimental design, Generative Recommenders, Production ML Systems, Computer Vision

Yelene Cisse