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Causal Inference Machine Learning Postdoctoral Jobs in Minnesota

Virtusa

Machine Learning Engineer

Minneapolis, MN · On-site

... Build training and inference code with reproducibility, versioning, and automated testing ... Role Summary:- Builds, trains and tunes machine learning models. Translates data science ...

Virtusa

Machine Learning Engineer

Minneapolis, MN · On-site

Role Summary:- Builds, trains and tunes machine learning models. Translates data science ... Build training and inference code with reproducibility, versioning, and automated testing ...

University of Minnesota

Epidemiologist

Minneapolis, MN

$112K - $145K/yr

Candidates are expected to have a deep understanding of causal inference, bias mitigation, and ... machine learning, digital health, wearables, etc. * Contribute to Epidemiology curriculum ...

Autodesk

Job Requisition ID # 26WD97132 26WD97132, Pr incipal Machine Learning Engineer, ML Platform and ... Guide the design of model deployment, inference services, monitoring, and observability for ...

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Causal Inference Machine Learning Postdoctoral information

What are the key skills and qualifications needed to thrive as a Causal Inference Machine Learning Postdoctoral researcher, and why are they important?

To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.

What are some common challenges faced by Causal Inference Machine Learning Postdoctoral researchers when integrating causal models with real-world data?

Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.

What is a Causal Inference Machine Learning Postdoctoral researcher?

A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.

What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Minnesota? For Causal Inference Machine Learning Postdoctoral jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Minnesota look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Minnesota are:
What cities in Minnesota are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities in Minnesota with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you
AI Experimental Systems Research Scientist (Causal Learning & Adaptive Experimentation)

AI Experimental Systems Research Scientist (Causal Learning & Adaptive Experimentation)

3M

Saint Paul, MN • On-site

Full-time

This job post has expired today. Applications are no longer accepted.

3M rating

8.0

Company rating: 8.0 out of 10

Based on 230 frontline employees who took The Breakroom Quiz

131st of 512 rated manufacturers

Job description

Overview AI Experimental Systems Research Scientist (Causal Learning & Adaptive Experimentation) at 3M. Collaborate with innovative 3Mers around the world. This position provides an opportunity to transition from other private, public, government or military experience to a 3M career.

As an AI Experimental Systems Research Scientist in 3M's Corporate R&D organization, you will work on a small, deeply technical team developing foundational methods for always-on learning systems that reason, experiment, and adapt in complex, non-stationary environments. This role focuses on preserving identifiability, causal validity, and epistemic calibration in learning systems, not merely performance. You will 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.

This is not a conventional data science or applied machine learning role; the work centers on how learning systems must structure experiments, manage interference and delayed effects, govern representations, and remain epistemically correct over time. This role is well suited for someone who enjoys working from first principles, designing rigorous experimental machinery, and translating statistical theory into systems that operate continuously in the real world. Responsibilities 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.

Working from whiteboards, research discussions, and evolving specifications—not fixed product requirements or static datasets. 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. Your Skills And Expertise To set you up for success in this role from day one, 3M requires (at a minimum) the following qualifications: 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.

Additional qualifications that could help you succeed even further in this role 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. Work location: Remote in the United States Travel: up to 20% domestic travel Relocation Assistance: May be authorized for relocation to Maplewood, MN Supporting Your Well-being 3M offers programs to help you live your best life – both physically and financially. To ensure competitive pay and benefits, 3M regularly benchmarks with other companies of similar size and scope.

Chat with Max – For assistance with searching current job openings or for more information about 3M, visit Max, our virtual recruiting assistant on 3M.com/careers. Applicable to US Applicants Only: The expected compensation range for this position is $141,150 - $172,517, which includes base pay plus variable incentive pay, if eligible. This range is a good faith estimate.

The specific compensation offered may vary based on factors including knowledge, training, skills, location, and experience. This position may be eligible for a range of benefits. Additional information is available at: https://www.3m.com/3M/en_US/careers-us/working-at-3m/benefits/ Good Faith Posting Date Range 03/09/2026 To 04/08/2026 Or until filled.

All US-based 3M full-time employees will need to sign an employee agreement as a condition of employment with 3M. This agreement covers confidentiality, trade secrets, conflicts of interest, and invention assignments. Employees at Job Grade 7 or above may have obligations regarding non-compete or non-solicitation during and after employment.

Learn more about 3M's solutions at www.3M.com or on social media @3M. Responsibilities of this position include compliance with corporate policies, procedures and security standards. Safety is a core value at 3M; all employees are expected to contribute to a strong EHS culture.

Pay & Benefits Overview: https://www.3m.com/3M/en_US/careers-us/working-at-3m/benefits/ 3M does not discriminate in hiring on the basis of race, color, sex, national origin, religion, age, disability, veteran status, or any other characteristic protected by law. Please note: your application may not be considered if you do not provide education and work history. 3M Global Terms of Use and Privacy Statement apply; review the Terms of Use and Privacy Policy for your country before applying.

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