As an AI Experimental Systems Research Scientist (Causal Learning & Adaptive Experimentation) at 3M, you will join a small, deeply technical team within the Corporate R&D organization. Your work will focus on developing foundational methods for always-on learning systems that reason, experiment, and adapt in complex, non-stationary environments, where experimentation and inference are first-class components of the learning process.
What You'll Do
- Design and implement adaptive experimental systems that operate continuously under nonstationarity, interference, and delayed or indirect outcomes.
- Develop causal estimands, randomization schemes, and inference procedures whose primary goal is identifiability and validity, not just reward optimization.
- Embed rigorous experimental control directly into learning systems, including experimentation on the system’s own learning mechanisms, parameters, and representational choices.
- Translate principles from experimental design, causal inference, and sequential decision-making into robust, always-on system behavior.
- Work from whiteboards, research discussions, and evolving specifications—not fixed product requirements or static datasets.
- Implement and maintain research code that supports hierarchical experimentation, baseline control streams, and statistically valid online inference.
- Create diagnostics, monitoring tools, and guardrails to ensure learning systems remain calibrated and do not stabilize spurious structure over time.
- Collaborate with interdisciplinary researchers to stress-test experimental learning mechanisms under realistic, adversarial conditions.
What We're Looking For
- A 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.
Nice to Have
- 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.
Technical Stack
- Python
Team & Environment
You will be part of a small, deeply technical team within 3M's Corporate R&D organization, collaborating with other curious, creative 3Mers in a supportive environment with a wide variety of people, global locations, technologies, and products.
Benefits & Compensation
- Compensation: $141,150 - $172,517
- Medical, Dental & Vision
- Health Savings Accounts
- Health Care & Dependent Care Flexible Spending Accounts
- Disability Benefits
- Life Insurance
- Voluntary Benefits
- Paid Absences
- Retirement Benefits
Work Mode
This role is remote within the United States.
3M does not discriminate in hiring or employment on the basis of race, color, sex, national origin, religion, age, disability, veteran status, or any other characteristic protected by applicable law.
