I'm an Astronomer turned Data Scientist in Fintech.
📈 First employee at a fintech startup as a Data Science Advisor and Educator
🔭 8+ YOE in quantitative data-driven research and analysis
📚 PhD in Astronomy & Astrophysics + HBSc in Astronomy & Physics from the University of Toronto
💬 Taught 500+ technical and non-technical students over 17 classes, including on the use of Python
📝 Published several data-driven papers in esteemed research journals
👥 Mentored a student on a year-long data project through to publication
🧵 Fun fact: I cross-stitch realistic astronomy observations on Etsy
Languages: Python (Scikit-Learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy), SQL (BigQuery, MySQL)
Tools: Docker, Tableau, Git/GitHub, Jupyter Notebook, MS Office Suite (Excel, PowerPoint, Word)
- Credit Card Application Prediction: Built a classification model to predict credit card application outcomes using logistic regression, k-nearest neighbors (KNN), and random forest, evaluating model performance using key classification metrics.
The following are smaller self-contained projects for learning core concepts:
- Predicting loan repayments using decision trees and random forest: Used tree-based models to predict loan repayment behavior based on borrower features.
- Classifying anonymized data with KNN: Used KNN to classify anonymized data into two categories, demonstrating the impact of feature scaling and k-value tuning.
- Predicting ad clicks with logistic regression: Modeled ad-click likelihood using demographic and behaviour information with logistic regression. Implemented feature engineering (including cyclical temporal feature mapping), multicollinearity reduction, threshold optimization, and model performance testing.
- Predicting synthetic credit scores with linear regression: Modeled synthetic credit scores based on financial and demographic features using linear regression. Explored feature engineering, correlation analysis, multicollinearity reduction, and statistical significance testing to evaluate feature importance and improve model interpretability.
- Insurance Analysis: Developed an interactive Tableau dashboard to report and analyze 70K insurance claims to support marketing and budget decisions.
- Marketing Analysis: Analyzed 100K e-commerce sales records using SQL (Google BigQuery) and Excel to uncover trends in customer behaviour, reporting sales and marketing metrics using an interactive Tableau dashboard.
- TTC Delay Analysis: Cleaned and analyzed 40K subway delay records for 2022-2023 using SQL and Tableau, assessing YoY KPIs and delay causes and providing performance improvement recommendations.