Member of Technical Staff, Causality

Responsibilities
Design and implement novel causal inference methods for treatment effect modeling.
Translate machine learning papers into production-ready code.
Build robust model evaluation frameworks.
Disseminate the results by co-authoring research papers and abstracts.
Collaborate with a multidisciplinary team of engineers and scientists.
Co-mentor junior members of the team.

Qualifications
PhD degree in causality, statistics or machine learning.
Deep understanding of causal inference methods and concepts.
Previous experience working with observational and randomized trial data.
Passion for research, attention to detail and ability to drive tasks to completion. Strong preference will be given to candidates with papers in A* conferences (e.g. ICML, ICLR, NeurIPS, CVPR) or top-tier statistics and causality journals.
Excellent understanding of core machine learning concepts.
Excellent knowledge of the foundations of statistics, linear algebra, probability and machine learning.
Excellent skills in Python and PyTorch.
Experience in deep learning. Experience in survival analysis, multi-modal learning, domain adaptation, model interpretability and computational pathology is a bonus.
Experience with medical data is a bonus.