Lead-ML Scientist AI For Clinical Trials & Competitive Intelligence

Key Responsibilities Develop and deploy AI/ML models to support: Clinical trial design and optimization Patient recruitment and stratification Site selection and enrollment prediction Clinical outcome and risk prediction Protocol optimization and operational efficiency Apply NLP, LLMs, and knowledge mining to analyze: Scientific literature Clinical trial databases Publications, patents, and conference abstracts Competitor pipelines and market intelligence Build competitive intelligence platforms to monitor therapeutic landscape, emerging technologies, clinical readouts, and competitor strategies. Generate predictive insights for portfolio prioritization, indication expansion, and go/no-go decisions. Develop automated AI workflows, dashboards, and data pipelines for clinical and strategic analytics. Collaborate with clinical development, medical affairs, regulatory, translational science, and business strategy teams. Qualifications MS/PhD in AI, Machine Learning, Data Science, Bioinformatics, Biostatistics, Computational Biology, or related field. 5-8+ years of experience in AI/ML, clinical analytics, healthcare, pharmaceutical, or biotech R&D. Technical Skills Strong expertise in machine learning, predictive modeling, NLP, LLMs, and data mining including retrieval-augmented generation (RAG) and LLM fine-tuning/prompt engineering for scientific and clinical text. Proficiency in Python, SQL, and ML frameworks (PyTorch, TensorFlow, Scikit-learn). Experience with clinical trial datasets, real-world data (RWD/RWE), medical literature mining, and competitive intelligence platforms. Experience with knowledge graphs, vector databases, and AI agents. Familiarity with clinical development, regulatory processes, and pharmaceutical R&D. Core Competencies Strong analytical thinking and strategic mindset. Ability to convert large datasets into actionable clinical and business insights. Strong communication and cross-functional collaboration skills. .