Principal Scientist, Predictive Modeling & Applied AI (Clinical Development)
Flatiron Health · NY · Research Sciences
About this role
Flatiron Health is hiring a principal-level Applied Scientist in the machine learning function based in NY. The posting calls out experience with Python, R, Deep Learning, Machine Learning.
- Role
- Applied Scientist
- Function
- machine learning
- Level
- principal
- Track
- Tech leadership
- Employment
- Full-time
- Location
- NY
- Department
- Research Sciences
More roles at Flatiron Health
Job description
from Flatiron Health careersReimagine the infrastructure of cancer care within a community that values integrity, inspires growth, and is uniquely positioned to create a more modern, connected oncology ecosystem.
We’re looking for a Principal Scientist in Predictive Modeling & Applied AI (Clinical Development) to help us accomplish our mission to improve and extend lives by learning from the experience of every person with cancer. Are you ready to be the next changemaker in cancer care?
What You'll Do
In this role, you will operate as a scientific leader within the Research Sciences (RS) organization, supporting our Scientific Engagement and Applied Research (SEAR) function, innovating and translating predictive modeling approaches—such as digital twins and other advanced simulation frameworks into decision grade solutions for pharmaceutical and academic partners. In this role you will propose model designs and select methodological approaches that achieve validity, interpretability, and fit-for-purpose use in clinical development. Specifically, you will:
- Lead the design, development, and validation of advanced predictive modeling solutions, for digital twins and other patient-level simulation approaches, in clinical development and adjacent use cases (e.g., trial design, cohort selection, endpoint prediction, treatment effect estimation, etc.)
- Advance a methodological strategy against existing and future use cases for applied AI in RWD/RWE by appropriate application of machine-learning, deep learning, causal inference, and multimodal modeling approaches