Machine Learning Scientist, Applied Machine Learning and Agentic AI, Pharma R&D
Tasks
- Apply survival analysis to oncology datasets
- Build deep agents with planning and tool use
- Collaborate with clinical scientists to define R D use cases
- Communicate technical results to external stakeholders
- Design scalable machine learning systems
- Develop agentic workflows
- Evaluate model performance using oncology metrics
- Integrate DNA RNA H E and clinical data into predictive models
- Orchestrate LLM applications for RAG
- Use causal inference frameworks for multimodal analysis
Perks/Benefits
Skills/Tech-stack
Causal Inference | Clinical Trial Data | Clinical trial | Cloud Machine Learning | CoxPH | Function Calling | Genomics | Git | Integrative Modeling | Langgraph | Language Models | Large Language Models | Machine Learning | Multimodal Embeddings | Multimodal Learning | Oncology | Pathology imaging | Prompt engineering | Python | Random survival forests | Real-World Evidence | Retrieval-Augmented Generation | Survival analysis | Transcriptomics | Trial data | Unit Testing
Education
Regions
Countries
States
Cities
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