Job Title: Data Scientist – AI-Powered Biomedical Data Analysis Location: Bronx, NY 10458 (Hybrid) Job Type: 1-Year Contract (with potential for extension) About the Role: We are seeking an experienced and innovative Data Scientist to lead the development and deployment of AI-driven solutions for biomedical literature and synthetic data analysis. This hybrid role is based in the Bronx, NY, and offers the opportunity to contribute to cutting-edge research at the intersection of artificial intelligence, life sciences, and healthcare. The successful candidate will work on designing and optimizing scalable AI models using open-source large language models (LLMs) to classify biomedical literature across multiple dimensions. This role plays a key part in generating insights that support evidence synthesis, systematic reviews, and clinical research. Key Responsibilities: Design and refine multi-dimensional classification frameworks (e.g., relevance, data type, medical category, application area) Implement Chain-of-Thought (CoT) reasoning and Few-Shot Learning (FSL) techniques Deploy and optimize open-source LLMs (e.g., LLaMA) for high-throughput biomedical text classification Perform inference on 5,000+ biomedical abstracts with attention to performance and accuracy Apply fine-tuning or low-rank adaptation (LoRA) for domain-specific model enhancement Curate high-quality training/validation datasets in collaboration with biomedical domain experts Standardize data preprocessing workflows to ensure consistency and integrity across datasets Identify trends in synthetic biomedical data applications, including methodology, disease focus, and clinical relevance Manage batch processing pipelines for large-scale text analysis using GPU-enabled infrastructure Present findings through reports, dashboards, and scientific presentations Required Qualifications: Master’s or PhD in Computer Science, Data Science, Biomedical Informatics, or related field 3+ years of experience in AI/ML projects, especially NLP and text classification Strong programming skills in Python Proficiency with machine learning libraries (e.g., PyTorch, TensorFlow) Experience with open-source LLMs (e.g., LLaMA, GPT) Knowledge of advanced prompting methods, such as CoT and FSL Ability to communicate effectively with both technical and non-technical stakeholders Strong problem-solving mindset and adaptability in a fast-paced R&D environment Preferred Skills: Familiarity with biomedical ontologies and data standards Experience with GPU-based model training and inference Knowledge of tools for data pipeline orchestration and high-throughput processing Prior work with systematic reviews, evidence synthesis, or literature mining