Hey, I'm Lindsey
Data Scientist
Avid learner and data analyst. I am passionate about using Python, Artificial Intelligence, and Machine Learning to uncover data-driven stories and solve business problems.
With a background in full-stack (MERN) development, I not only analyze data but also build the useful, interactive applications that bring those insights to life. My work is focused on creating real-world value, from predicting customer churn to analyzing market sentiment. Check out my latest data science projects below and reach out!
My Skills
My Expertise
Programming Languages
Python, R, SQL
Data Science & Machine Learning
Pandas, NumPy, Scikit-learn, EDA, Statistical Analysis, Data Visualization, Data Cleaning
Tools & Platforms
Google Colab, Jupyter Notebook, VS Code, GitHub
Web Development
JavaScript, React, MERN Stack
About
About Me
Hi, I'm Lindsey. I'm a Data Analyst in Austin, TX, with a passion for bridging the gap between business strategy and data-driven insights. After more than a decade in business operations and technical talent strategy, I'm leveraging my new specialization in machine learning and data science to solve complex challenges.
I'm currently pursuing my M.S. in Data Science from the University of Virginia and hold recent certificates in AI/Machine Learning and Full Stack Development from UT Austin. My experience allows me to see the full picture—I don't just build predictive models with Python, SQL, and Scikit-learn, I understand the "why" behind the data, focusing on stakeholder needs and delivering measurable ROI.
I'm excited to bring this unique blend of deep business experience and advanced technical skill to a team focused on solving meaningful problems.
Stock News Sentiment & Summarization
Built an AI-driven system to analyze stock news sentiment and generate weekly summaries. This project used a Random Forest classifier trained on SentenceTransformer embeddings for sentiment analysis and Llama-2 for automated text summarization.
Bank Customer Churn Prediction
Developed a deep neural network classifier to predict bank customer churn. This project involved building, training, and evaluating multiple architectures in Keras (TensorFlow), optimizing the final model with Adam and Dropout to achieve 86% accuracy.
Personal Loan Campaign
Developed a predictive model to identify customers likely to accept a personal loan. This project used a Decision Tree Classifier optimized with post-pruning to improve generalization, achieving 98.5% accuracy and 91.3% recall.
Food Aggregator App (EDA)
Conducted an exploratory data analysis on 1,800+ orders to find the causes of low customer ratings. Used statistical (univariate and bivariate) analysis to correlate operational data with ratings, discovering that delivery and prep times over 25 minutes were the primary driver of low scores.
Workout Placita
A full-stack MERN social fitness app where users can create accounts, log notes on video workouts, and connect with friends to share routines and feedback.
React Portfolio
A single-page portfolio (this site) built from scratch using React and Vite, featuring a custom layout and reusable React components..
Get in Touch
Contact Me
I'd love to hear from you about employment, freelancing, and networking.