For our headquarters in Munich we are seeking highly qualified candidates to fill the position:

Intern (m/f/d) Artificial Intelligence 

The Position:

This internship offers an opportunity to engage with cutting-edge research in Causal Inference using Machine Learning (ML), focusing on advanced methodologies including, but not limited to, Doubly Robust Learners and Double Machine Learning (Double ML) using Bayesian Additive Regression Trees (BART) and other machine learning methods. By leveraging flexible ML techniques for modeling both treatment assignment and outcomes, these methods aim to correct for complex, non-linear confounding biases, thereby enabling more precise estimation of causal effects such as Average Treatment Effects (ATE), Conditional Average Treatment Effects (CATE), and Individual Treatment Effects (ITE).

Roles and Responsibilities:

• Conduct a comprehensive review of existing ML-based causal inference methods, assessing their suitability for different types of causal effect estimation and data characteristics.
• Evaluate state-of-the-art causal inference techniques on benchmark datasets and contribute to the development of a robust toolkit for causal inference in evidence generation.
• Implement and apply Double ML methods, with a focus on conducting sensitivity analyses to assess robustness of causal estimates.
• Prepare and deliver a presentation summarizing findings and insights to the team.

Personal Skills and Professional Experience:

• Basic causal inference knowledge: propensity scores, IPW estimators, propensity score matching, observational data, ATE, etc.
• Familiarity with machine learning and Data Science methodology.
• Interest in diving into causal inference using Machine Learning techniques.
• Skilled in Python programming for Data Science and Machine Learning.
• Some R programming experience.
• Strong analytical and problem-solving skills.
• Excellent communication skills with a proficient English level.
• Ability to work effectively in a collaborative team environment.
• Strong attention to detail and the ability to manage and organize data.