AI & Machine Learning Specialist | Finance & Data Science
Master's in Economics · AI/ML Practitioner Certificate (University of Waterloo)
I build machine learning solutions that solve real business problems — from credit risk modeling to time series forecasting to natural language processing. My background bridges quantitative economics, finance operations, and applied AI, giving me a unique ability to translate complex business challenges into data-driven solutions.
Currently working in Finance at Seaspan ULC (Shipbuilding & Maritime), where I apply analytical and AI-driven approaches to operational and financial process improvement.
| Domain | Techniques |
|---|---|
| Supervised Learning | Logistic Regression, Random Forest, XGBoost, Gradient Boosting, Hyperparameter Tuning |
| Unsupervised Learning | K-Means Clustering, PCA, Dimensionality Reduction, Anomaly Detection |
| Deep Learning | LSTM, CNNs, Transformers, Sequence Modeling |
| NLP | Text Classification, Attention Mechanisms, Transformer Architectures |
| Data Analysis & EDA | Statistical Testing, Feature Engineering, Visualization, Correlation Analysis |
| Project | Description | Technologies |
|---|---|---|
| CNN Image Recognition | Convolutional Neural Network achieving 99%+ accuracy on MNIST handwritten digit recognition | TensorFlow, Keras, CNN, Computer Vision |
| MNIST Regularization | Comprehensive analysis of dropout, batch normalization, and L2 regularization to prevent overfitting | TensorFlow, Keras, Regularization |
| Deep Neural Network | Multi-layer perceptron for digit classification with 98%+ accuracy | TensorFlow, Keras, Deep Learning |
| Project | Description | Technologies |
|---|---|---|
| Bitcoin Price Prediction | LSTM and GRU models for cryptocurrency price forecasting using sequential pattern recognition | TensorFlow, LSTM, GRU, Time Series |
| Stock Price Prediction (ET) | Bidirectional RNN for Energy Transfer stock forecasting with temporal analysis | TensorFlow, Bidirectional RNN, Financial Analysis |
| Project | Description | Technologies |
|---|---|---|
| Frozen Lake RL | Q-learning and value iteration implementation for optimal policy learning | Gymnasium, Value Iteration, Q-Learning |
| Project | Description | Technologies |
|---|---|---|
| Transformer from Scratch | Educational implementation of Transformer architecture with multi-head attention | PyTorch, Attention Mechanisms, NLP |
| Project | Description | Technologies |
|---|---|---|
| Loan Default Prediction | Credit risk assessment model to identify high-default probability borrowers | XGBoost, Random Forest, Classification |
| Customer Churn Prediction | Machine learning model predicting customer churn from behavioral patterns | Decision Trees, Random Forest, XGBoost |
| NYC Taxi Analytics | Predictive modeling for taxi tipping behavior and fare optimization | Scikit-learn, EDA, Regression |
| Project | Description | Key Methods |
|---|---|---|
| Coming soon |
Deep Learning: TensorFlow, Keras, PyTorch | CNN, RNN, LSTM, GRU, Transformers
Machine Learning: Scikit-learn, XGBoost, Random Forest, Gradient Boosting
Data Science: Pandas, NumPy, Matplotlib, Seaborn, Jupyter
Specializations: Time Series Forecasting, Computer Vision, NLP, Reinforcement Learning, Credit Risk Modeling
Tools: Python, SQL, Git, Tableau, Power BI
- 🎓 M.S. Economics — Illinois State University
- 📜 AI/ML Practitioner Certificate — University of Waterloo
- 🚢 Seaspan ULC — Finance & Data Science (Shipbuilding & Maritime)
- 📈 Seeking Alpha — Financial Research & Investment Analysis
- 💻 Dotin — Financial Software Development
Open to opportunities in data science, machine learning, and economic consulting.