PhD in causality, statistics, or machine learning.
Deep knowledge of causal inference methods.
Experience with observational and randomized trial data.
Demonstrated research passion and completion focus; preference for A* conference publications.
Strong grasp of machine learning, statistics, linear algebra, and probability principles.
Proficient in Python and PyTorch for model building and experimentation.
Familiarity with deep learning and additional areas such as survival analysis and model interpretability is advantageous.
Responsibilities
Design and execute innovative causal inference methodologies.
Convert theoretical machine learning research into practical code.
Establish solid frameworks for evaluating models.
Share findings through co-authored research papers and abstracts.
Work collaboratively with a diverse team of engineers and scientists.
Mentor junior team members to foster their growth.
Benefits
Collaborative work environment with diverse teams.
Opportunity for publication and research dissemination.
Access to cutting-edge technology and methodologies.
Flexible work arrangements to support work-life balance.
Full Job Description
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.
