Are you interested in working on fascinating scientific and engineering challenges of modern information technology? Would you like to contribute to the development of the future generation of cloud computing at Amazon Web Services?
As an Applied Scientist, you will be working on cutting edge projects in the intersection of causal inference, machine learning, and high-dimensional statistics. You will be part of an ambitious team of scientists and software engineers that is together developing novel software products for world-wide use.
The AWS Causality Lab is located at the Tübingen site in Germany. Our goal is to enable our customers to improve confidence in their data science conclusions by making the underlying cause-effect relationships explicit. Going beyond mere correlational analysis, we quantify the causes of observations, and provide actionable insights based on data-driven what-if predictions.
Our mission is to provide automated software for causal inference to our customers which builds on formalisms, algorithms, and statistical guarantees.
As an Applied Scientist in the Causality Lab, you will be responsible for:
- research and development of algorithms in causal inference
- analyzing different data types, including time series, textual data sources and graphs
- infering causal relationships between these inputs, and discriminating these from coincidental correlations
- identifying the causes of particularities in data and quantify their specific contributions to downstream metrics
- infering interventional and counterfactual analysis
- collaborating product and development teams across AWS and Amazon as well as directly with customers
- Progress towards a completed PhD in Computer Science, Mathematics, Physics, Statistics or any other field with strong quantitative focus
- Demonstrated experience in transforming theoretical concepts into consumer products
- Proven coding experience both prototyping new methods and building industry-standard software
- Proven written and verbal communication skills in English
- Record of publications at well-regarded conferences and in associated journals in either machine learning (ML), causality, statistics, information theory or probability theory
- Experience with cloud computing products
- Business experience
- Experience with causal inference and high-dimensional statistics
- Expertise on a broad range of machine learning methods
- Excellent problem solving ability
- Fascination for conceptual problems raised by causal inference
- Demonstrated experience with hiring and/or mentorship of junior scientists