
Worked on the d2cml-ai/Data-Science-Python repository to deliver two core features focused on extracting insights from financial documents and optimizing machine learning workflows. Developed a pipeline that combines FinBERT-based sentiment analysis with PDF text extraction and summarization, enabling rapid review of financial texts and contracts. Built an ML experimentation suite to benchmark CPU versus GPU training times for both a Fashion MNIST CNN and a DistilBERT toxicity classifier, supporting data-driven resource planning. Leveraged Python, TensorFlow, and Hugging Face Transformers throughout the project, maintaining reproducible Jupyter Notebook workflows to facilitate scaling and future experimentation in deep learning and NLP tasks.
June 2025 performance summary for d2cml-ai/Data-Science-Python: Delivered two core features and established a benchmarking framework to inform resource planning. Key business value: faster extraction of insights from financial texts and contracts; data-driven capacity planning for ML workloads.
June 2025 performance summary for d2cml-ai/Data-Science-Python: Delivered two core features and established a benchmarking framework to inform resource planning. Key business value: faster extraction of insights from financial texts and contracts; data-driven capacity planning for ML workloads.

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