AI/ML Automation Pipelines Intern

Responsibilities The AI/ML Automation Intern will explore how AI and machine learning can streamline programming workflows in clinical trial reporting. The focus will be on building proof-of-concept automation pipelines that use Python and ML models to improve efficiency, accuracy, and standardization. Objectives: • Learn how AI/ML can be integrated into clinical data workflows. • Build automation pipelines to reduce manual coding and enhance prediction accuracy. • Apply Python programming and ML frameworks (e.g., scikit-learn, LangChain) to real-world data challenges.

The AI/ML Automation Intern will explore how AI and machine learning can streamline programming workflows in clinical trial reporting. The focus will be on building proof-of-concept automation pipelines that use Python and ML models to improve efficiency, accuracy, and standardization. Objectives: • Learn how AI/ML can be integrated into clinical data workflows. • Build automation pipelines to reduce manual coding and enhance prediction accuracy. • Apply Python programming and ML frameworks (e.g., scikit-learn, LangChain) to real-world data challenges. Key Deliverables: 1. Develop a prototype ML-based code recommender for TLF (Tables, Listings, Figures) generation. 2. Automate study metadata extraction from protocols or SAPs using NLP models. 3. Create a Python-based monitoring dashboard for QC metrics across multiple studies. 4. Present findings and demonstrate automation pipeline in a showcase session.

Objectives: • Learn how AI/ML can be integrated into clinical data workflows. • Build automation pipelines to reduce manual coding and enhance prediction accuracy. • Apply Python programming and ML frameworks (e.g., scikit-learn, LangChain) to real-world data challenges. Key Deliverables: 1. Develop a prototype ML-based code recommender for TLF (Tables, Listings, Figures) generation. 2. Automate study metadata extraction from protocols or SAPs using NLP models. 3. Create a Python-based monitoring dashboard for QC metrics across multiple studies. 4. Present findings and demonstrate automation pipeline in a showcase session. Qualifications

Qualifications Currently pursuing Masters or PhD in Biostatistics, Computer Science or relevant fields with analytics focus. Strong experience with R, R Shiny, Python and AI/ML is required. Daiichi Sankyo, Inc. is an equal opportunity/affirmative action employer. Qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected veteran status, age, or any other characteristic protected by law.