Would you like to play a critical part in the next revolution of human-computer interaction? Would you like to contribute to the advancement of a product that is globally redefining how humans use voice to interact with technology? The Siri Data organization seeks to improve Siri by using data as the voice of our customers. We are looking for an architect operating at the intersection of causal inference and engineering to help us build software and engineering systems that will allow the Siri Data organization to use cutting edge methods from causal inference research in a way that is scalable for engineering systems and also productive for data science teams. Were looking for an exceptional data scientist/engineer who can successfully function as an interdisciplinary & cross functional bridge between the worlds of data science and engineering. A successful candidate will identify as both an engineer and data scientist. In this role the candidate will design the data science tools that our data scientists use to analyze causal effects; they will help architect & guide a scalable experimentation system that uses causal models to measure average causal effects, segmentation in effects, and time trends in effects all while scaling to hundreds of experiments with petabytes of data; and finally they will support personalization systems where a better understanding of segmentation in causal effects will directly lead to smarter personalization strategies. To accomplish this, you will need to effectively partner with machine learning, infrastructure, data science, and data engineering teams.
On any given day you will be ...
- Moving between understanding the open & unanswered questions about the Siri Assistant & Search products; to researching new causal inference methods; to designing & building systems that use those methods at extreme scale.
- Working with data infrastructure teams providing feedback to improve our platform.
- Working with experimentation methodology and platform teams to both specify requirements and to support the use of advanced statistical techniques.
- Working with ML teams to determine how a deeper awareness of segmentation in causal effects will improve personalization and specify requirements for engineering systems rooted in causal reasoning.
- Broadly partnering with your teammates across the AI/ML organization to understand their requirements, to answer questions, to provide support, and to innovate in taking our data science capabilities to the next level.
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