I am an Applied Mathematics and Statistics MSc graduate who is passionate about machine learning, mathematics, and data science. With experience in cloud technologies, data visualization, and automation, I'm always excited to tackle challenging projects that create real impact.
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M.S. in Applied and Computational Mathematics and Statistics
- University of Notre Dame, IN
- Graduated: May 2023
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B.S. in Applied and Computational Mathematics and Statistics
- University of Notre Dame, IN
- Minor: Actuarial Science
- Graduated: May 2022
Jan 2025 - Present
- Joining the TMCI Data Quality Group to design ETL pipelines, implement data warehousing solutions, and develop data integration processes using Python, SQL, and modern data modeling techniques.
Aug 2023 - Dec 2024
- Built and optimized ETL pipelines for 300,000+ SKUs across 8 brands in 6 countries, ensuring timely and accurate data availability for stakeholders.
- Established a PostgreSQL data warehouse integrating data from SQL Server, BigQuery, and MongoDB, including data modeling and normalization, to enhance data accessibility and analytics.
- Created data visualisation dashboards.
Aug 2022 - Dec 2022
- Assisted with “Introduction to Probability” and “Statistical Learning for Data Science.”
Jun 2022 - Aug 2022
- Analyzed financial statements to identify investment opportunities, creating financial models and Python visualizations to support data-driven decisions.
- Redesigned Excel-based tools to streamline investment analysis processes, resulting in improved accuracy and faster reporting, and utilized Bloomberg Terminal to gather key financial metrics.
A minimal implementation of the Keras Sequential model and Dense Layer classes, built from scratch using Python and NumPy.
- Docs: microkeras.readthedocs.io/en/latest
- GitHub: MicroKeras Repository
- Colab Demo: Open in Colab
Trained models using Google Cloud AutoML, TensorFlow, and XGBoost to price European options and compared performance against the Black-Scholes model.
- GitHub: Options Pricing Repository
- Research Paper: arXiv
- Programming Languages: Python, SQL, C++, Java, JavaScript
- Libraries / Frameworks: Pandas, PySpark, SciKit-Learn, PyTorch, Django, React, Tailwind CSS
- Databases / Datawarehouses: PostgreSQL, SQL Server, MongoDB, BigQuery, Hive
- Data Visualization: PowerBI, Tableau, Streamlit, Plotly Express
- Tools / Platform: Docker, Kubernetes, Apache Airflow, Linux, Neovim