Recent graduate of McGill University’s Master of Management in Analytics, with a non‑traditional path into data after four years as a pharmacist. In healthcare I managed operations, solved complex problems under pressure, and developed a rigorous, scientific approach to decision‑making.
Transitioning into data required me to learn and adapt rapidly. At the end of the Master's program, I was honored to receive the Top Student Award, reflecting perseverance, learning agility, and the ability to perform alongside peers with more conventional technical backgrounds. I built machine learning models from the ground up—spanning supervised/unsupervised learning and deep learning (forecasting, classification, NLP, and computer vision).
Today, I channel that mindset into data science and ML. I enjoy mastering new tools, breaking down ambiguous problems, and turning data into clear, actionable insights. My focus is on building scalable, dependable models and end‑to‑end pipelines that integrate into real systems, sharpen decision‑making, and improve business outcomes—especially in fast‑moving, innovation‑driven environments.
- Languages: Python, SQL, R
- ML/DL: Supervised & Unsupervised Learning, Causal Inference, Time-Series, Scikit‑learn, PyTorch, SHAP, LIME
- Data Platforms & ML Tools: MLflow, Git, Docker, Google BigQuery, PostgrelSQL, MySQL
- Visualization: Power BI, Tableau, Streamlit
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