Job Description:
• Collaborate closely with researchers across statistics, cognitive science, and machine learning to design systems in which experimentation, inference, and uncertainty are first-class components of the learning process itself.
• Designing and implementing adaptive experimental systems that operate continuously under nonstationarity, interference, and delayed or indirect outcomes.
• Developing causal estimands, randomization schemes, and inference procedures whose primary goal is identifiability and validity, not just reward optimization.
• Embedding rigorous experimental control directly into learning systems, including experimentation on the system’s own learning mechanisms, parameters, and representational choices.
• Translating principles from experimental design, causal inference, and sequential decision-making into robust, always-on system behavior.
• Implementing and maintaining research code that supports hierarchical experimentation, baseline control streams, and statistically valid online inference.
• Creating diagnostics, monitoring tools, and guardrails to ensure learning systems remain calibrated and do not stabilize spurious structure over time.
• Collaborating with interdisciplinary researchers to stress-test experimental learning mechanisms under realistic, adversarial conditions.
Requirements:
• Ph.D. in Statistics, Biostatistics, Economics, Computer Science, Data Science, Operations Research, or a closely related field (completed and verified prior to start).
• Deep grounding in experimental design and statistical inference, including randomized experiments and causal estimands.
• Demonstrated ability to implement research-grade statistical or experimental methods in a general-purpose programming language (e.g., Python).
• Experience working in research settings where the problem definition evolves and correctness takes precedence over convenience.
• Experience with adaptive or sequential experimentation (e.g., response-adaptive trials, causal bandits, best-arm identification).
• Familiarity with causal inference frameworks spanning both design-based and model-based approaches.
• Strong intuition for identifiability, bias–variance tradeoffs, and statistical validity in complex, real-world settings.
• Experience working with nonstationary systems, concept drift, or delayed feedback loops.
• Experience reasoning about interference, carryover effects, time-varying treatments, or non-independent experimental units.
• Comfort designing experiments where the learning process itself is the object under experimental control.
• Familiarity with hierarchical or clustered experimental designs and multi-level inference.
• Interest in foundational questions about how autonomous systems should reason, experiment, and adapt in the world.
• Ability to communicate complex statistical ideas clearly to interdisciplinary collaborators.
• Curiosity, intellectual humility, and a strong preference for epistemic correctness over short-term performance gains.
Benefits:
• Medical
• Dental & Vision
• Health Savings Accounts
• Health Care & Dependent Care Flexible Spending Accounts
• Disability Benefits
• Life Insurance
• Voluntary Benefits
• Paid Absences
• Retirement Benefits