Data Scientist II, Real World Evidence (RWE), Pharma R&D
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La posizione
Independent lead for observational real-world studies in oncology to address questions in trial design and outcomes research.
Derive and interpret complex endpoints from real-world data, applying survival analysis, causal inference, and related methods.
Partner with pharmaceutical collaborators to translate analyses into actionable insights for patient selection and treatment strategies.
Leverage AI tools, LLMs, and agentic workflows to accelerate code, documentation, and insight generation.
Contribute reusable code, internal packages, and methodological best practices while maintaining rigorous software engineering standards.
Communicate complex methods and results clearly to technical and non-technical stakeholders and prepare reports or manuscripts when appropriate.
Derive and interpret complex endpoints from real-world data, applying survival analysis, causal inference, and related methods.
Partner with pharmaceutical collaborators to translate analyses into actionable insights for patient selection and treatment strategies.
Leverage AI tools, LLMs, and agentic workflows to accelerate code, documentation, and insight generation.
Contribute reusable code, internal packages, and methodological best practices while maintaining rigorous software engineering standards.
Communicate complex methods and results clearly to technical and non-technical stakeholders and prepare reports or manuscripts when appropriate.
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