RESUME_BULLETS.md

Resume Bullets Generator

Select the version that best fits the role you are applying for.

1-Line Version (For summary sections)

• Architected an End-to-End ML E-Commerce Platform featuring GPU-accelerated segmentation, time-series forecasting, and streaming fraud detection deployed via Streamlit.

3-Line Version (Standard)

• E-Commerce ML Platform: Engineered an end-to-end data platform unifying 10+ relational databases to drive insights via 5+ ML pipelines (Churn, Demand, Segmentation, NLP, Fraud).
• GPU Acceleration & Optimization: Leveraged RAPIDS cuML and CuPy for 10x faster clustering and matrix factorization, optimizing hyperparameters using Nested Cross-Validation and Optuna.
• Production Deployment: Designed a streaming SGDClassifier for real-time anomaly detection and deployed all models into an interactive Streamlit application.

ATS Optimized Version (Keyword dense)

• Developed an End-to-End Machine Learning platform using Python, Scikit-Learn, XGBoost, and LightGBM to predict customer churn, optimize time-series demand forecasting, and perform NLP sentiment analysis.
• Implemented GPU-accelerated clustering (PCA, K-Means, RAPIDS) and recommendation systems (Collaborative Filtering, Funk SVD, CuPy) to enhance marketing personalization.
• Engineered a streaming data pipeline for fraud detection using Isolation Forests and SGDClassifier, ensuring target leakage prevention and explaining models with SHAP and LIME.

Data Scientist Version (Focus on algorithms & rigor)

• Engineered a stacking classifier (XGBoost, LightGBM, Logistic Regression) to predict conversion, strictly preventing target leakage and handling class imbalance via intra-CV SMOTE.
• Wrote custom Funk SVD matrix factorization from scratch using CuPy to leverage GPU compute for massive collaborative filtering datasets.
• Optimized time-series LightGBM models via 30-trial Optuna Bayesian Search within a Nested Cross-Validation loop to ensure robust generalization.

Data Analyst Version (Focus on insights & business value)

• Designed an automated RFM (Recency, Frequency, Monetary) segmentation pipeline, identifying 4 distinct customer personas to drive targeted marketing campaigns.
• Extracted actionable insights from Portuguese customer reviews using TF-IDF and LinearSVC, enabling automated triage of urgent support tickets.
• Consolidated disparate operational data into a unified SQLite database, conducting leakage-free EDA and building interactive Streamlit dashboards for stakeholders.

Internship / Entry-Level Version (Focus on learning & initiative)

• Built a comprehensive Machine Learning portfolio project demonstrating the full data science lifecycle, from SQL data ingestion to model deployment.
• Proactively learned and implemented advanced techniques typically reserved for senior roles, including Streaming Learning and Model Explainability (SHAP/LIME).
• Independently researched and applied GPU-acceleration libraries to overcome computational bottlenecks in standard scikit-learn models.