The Bayesian and Causal Modeling team is responsible for developing, documenting, and distributing efficient analytic algorithms that support causal inference workflows and Bayesian modeling and analysis. We engage in methodological development and computational engineering to incorporate cutting-edge innovations into statistical software implementation. In addition, we provide user support to ensure reliable and impactful application of these tools in real-world applications.
As an intern, you might:
- Conduct literature reviews on cutting-edge topics at the intersection of Bayesian statistics and AI.
- Develop and prototype new Bayesian and AI methods or computational procedures for a selected research topic.
- Implement examples or simulations in Python, R, C/C++, or other programming languages to test the feasibility and performance of proposed approaches.
- Collaborate with team members to evaluate the effectiveness of new methods, compare them to existing approaches, and refine implementation details.
Required Qualifications
- You are a PhD student (2nd year or beyond). studying Statistics, Computer Science, Applied Mathematics, Machine Learning, or a related quantitative field, not graduating prior to December 2026.
- You have programming experience in one or more of the following languages: Python, R, Java, C, or C++.
- You have a strong foundation in Bayesian or probabilistic modeling methods relevant to AI-driven analytics.
- You’re curious, passionate, authentic, and accountable. These are our values and influence everything we do.
- Strong communication skills – both written and verbal.
- Leadership abilities. Your past experiences demonstrate you’ll take initiative and go above and beyond the call of duty.
- You’re interested in the future of Analytics and embrace technology.