Member of Technical Staff, Causality

The role

This person will design and implement novel causal inference methods for treatment effect modeling. They will translate machine learning papers into production-ready code and build model evaluation frameworks.

What you will do

• 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.

Required 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.
• 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.

Nice to have

• Papers in A* conferences (e.g. ICML, ICLR, NeurIPS, CVPR) or top-tier statistics and causality journals.
• Experience in survival analysis, multi-modal learning, domain adaptation, model interpretability and computational pathology.
• Experience with medical data.

About the company

Ataraxis is a clinical AI research lab working at the intersection of multi-modal AI and precision medicine. The company develops AI methods to predict patient outcomes and treatment response.