Cody Chapman

I am a results-driven Data Scientist and Machine Learning Engineer with experience building production AI systems at Fortune-500 companies and research labs. I specialize in deep learning, large-scale data pipelines, and MLOps, and I deliver measurable business value.

At Fidelity I developed a hybrid XGBoost + LSTM fraud model that reduced false positives by 42% and produced $2.3M in annual savings, and I engineered Spark + AWS Glue pipelines processing 10+ TB of daily transaction data. I also designed a company-wide A/B testing platform adopted by multiple business units.

In research and internships I have fine-tuned multilingual transformer models achieving high cross-language accuracy, led graph neural network work (NeurIPS 2025 paper accepted), and authored internal best-practice guides. I mentor others, secure research funding, and build dashboards and tools adopted by hundreds of analysts.

I seek full-time roles in generative AI, computer vision, and autonomous systems where I can apply my strengths in model development, deployment, and scalable data infrastructure to solve high-impact problems.