Scientific team dynamics are critical in determining the nature and impact of research outputs. However, existing methods for classifying author roles based on self-reports and clustering lack comprehensive contextual analysis of contributions. Thus, we present a transformative approach to classifying author roles in scientific teams using advanced large language models (LLMs), which offers a more refined analysis compared to traditional clustering methods. Specifically, we seek to complement and enhance these traditional methods by utilizing open source and proprietary LLMs, such as GPT-4, Llama3 70B, Llama2 70B, and Mistral 7x8B, for role classification. Utilizing few-shot prompting, we categorize author roles and demonstrate that GPT-4 outperforms other models across multiple categories, surpassing traditional approaches such as XGBoost and BERT. Our methodology also includes building a predictive deep learning model using 10 features. By training this model on a dataset derived from the OpenAlex database, which provides detailed metadata on academic publications -- such as author-publication history, author affiliation, research topics, and citation counts -- we achieve an F1 score of 0.76, demonstrating robust classification of author roles.