Data Scientist, Life Sciences AI

- PhD preferred (or Masters with 8+ years of experience) in Biostatistics, Bioinformatics, Computer Science, Health Informatics, or a related field. - Profound knowledge of statistical modeling, meta-analysis methodologies, and evidence synthesis techniques applicable to post-market and real-world evidence (RWE) scenarios. - Strong expertise in NLP and AI principles, particularly concerning LLMs, prompt engineering, and information extraction from scientific literature. - Proficiency in Python and major ML/NLP frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, and scikit-learn. - Verified experience in designing and implementing AI-driven analytical tools or platforms, including LLM-based workflows for literature mining, evidence synthesis, or clinical data interpretation. - Familiarity with vector databases (e.g., Pinecone, PostgreSQL) and knowledge graph or semantic data modeling for organizing biomedical and clinical information. - Solid understanding of experimental design, statistical inference, and validation methods to ensure scientific integrity in software outputs. - Background in biomedical or healthcare data analysis, including the integration of real-world data sources such as literature, EHR, claims, or registry data. - Knowledge of data privacy, ethics, and regulatory standards in healthcare (e.g., HIPAA). - Established ability to collaborate effectively with engineers, researchers, domain experts, and clients. - Exceptional communication, documentation, and presentation skills; demonstrated mentorship experience and contributions to scientific publications or conference presentations in the life sciences. - Inquisitive, innovative, and solution-oriented mindset with a passion for creating tools that enhance evidence generation and decision-making in life sciences.