Collaborate 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
Deep grounding in experimental design and statistical inference
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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

experimental designstatistical inferencePythonadaptive experimentationsequential experimentationcausal inferencenonstationary systemsconcept driftmulti-level inferencestatistical validity

Soft Skills

communicationcuriosityintellectual humilityepistemic correctnesscollaboration

Certifications

Ph.D. in StatisticsPh.D. in BiostatisticsPh.D. in EconomicsPh.D. in Computer SciencePh.D. in Data SciencePh.D. in Operations Research