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Ai Causal Inference Jobs (NOW HIRING)

The Voleon Group

We apply state-of-the-art AI/ML techniques to construct our liquidity-provision strategies. For ... Financial applications of causal inference are challenging and demand new methods that go beyond ...

Statistics Interest Group

The Liu Lab builds on a strong track record in AI-powered causal inference, Bayesian generative models, and computational genomics, with recent work published in leading journals, such as JASA ...

Alembic

Research Engineer - Causal AI

San Francisco, CA · On-site

$200K - $250K/yr

Build production systems for causal inference that maintain statistical rigor at enterprise scale ... Work with advanced AI/ML algorithms, composite AI solutions, private NVIDIA DGX clusters, and the ...

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How much do ai causal inference jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for ai causal inference in the United States is $56.81, according to ZipRecruiter salary data. Most workers in this role earn between $46.63 and $67.31 per hour, depending on experience, location, and employer.

What is an AI causal inference professional?

AI Causal Inference professionals specialize in using artificial intelligence and statistical methods to determine cause-and-effect relationships within data. Unlike traditional data analysts who may focus on correlations, these experts design experiments or apply mathematical models to uncover how changes in one variable influence another. Their work is crucial in fields like healthcare, economics, and social sciences, where understanding causality can inform better decisions and policies. They often use tools like causal diagrams, randomized controlled trials, and advanced machine learning techniques to draw robust conclusions.

What are the key skills and qualifications needed to thrive as an AI causal inference specialist?

To thrive as an AI Causal Inference Specialist, you need a strong background in statistics, machine learning, and causal modeling, typically supported by an advanced degree in a quantitative field. Familiarity with programming languages like Python or R, experience with causal inference libraries (such as DoWhy or CausalNex), and knowledge of statistical software are commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help you interpret complex results and collaborate across multidisciplinary teams. These skills ensure accurate causal analysis, actionable insights, and reliable decision-making in data-driven environments.

What are some common challenges faced by professionals working in AI causal inference, and how can they be addressed?

Professionals in AI causal inference often encounter challenges such as dealing with incomplete or biased data, distinguishing correlation from true causation, and communicating complex findings to non-technical stakeholders. Addressing these challenges typically involves leveraging robust statistical methods, collaborating closely with domain experts, and maintaining transparency in modeling decisions. Continuous learning and staying updated with the latest research can also help navigate the rapidly evolving landscape of AI causal inference.

What other helpful pages are available for Ai Causal Inference?

Other pages related to Ai Causal Inference:

Infographic showing various Ai Causal Inference job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 76% Full Time, 19% Part Time, and 4% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution, with an average salary of $118,171 per year, or $56.8 per hour.

Postdoctoral Scholar in Health AI-Pediatrics CBMI

Memphis, TN • On-site

The University of Tennessee
Colleges, Universities, and Professional Schools • 1 - 5K employees
Learn more about The University of Tennesseeexternal

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Job description


THIS IS A GRANT-FUNDED POSITION FUNDED UNTIL OCTOBER 1, 2027.
The Postdoctoral Scholar designs, develops, evaluates, and optimizes advanced artificial intelligence (AI) methods that support intelligent cancer patient navigation and clinical decision support. Under the direction of the Principal Investigator, this position leads research and development on multimodal machine learning, agentic AI, explainable AI, causal inference, predictive analytics, and continuous learning to develop trustworthy AI solutions that improve cancer care delivery.
Responsibilities
  1. Designs, develops, implements, and optimizes advanced artificial intelligence, machine learning, and multimodal AI models for intelligent cancer patient navigation and clinical decision support.
  2. Conducts research in explainable AI, agentic AI, causal inference, predictive analytics, knowledge representation, and continuous learning.
  3. Designs and evaluates AI algorithms using electronic health records, patient-reported outcomes, social determinants of health, medical imaging, and other healthcare data sources.
  4. Conducts benchmarking, validation, performance evaluation, and fairness, robustness, and explainability assessments of AI model.
  5. Collaborates with software engineers, clinicians, and interdisciplinary investigators to translate AI research into interoperable clinical applications and decision support tools.
  6. Develops and maintains reproducible analytical workflows and research software to support AI model development and evaluation
  7. Mentors graduate students and junior researchers throughout the project lifecycle.
  8. Prepares manuscripts, technical reports, conference presentations, and publications in leading journals and scientific meetings.
  9. Participates in proposal preparation and collaborative research activities supporting federally and state-funded research programs.
  10. Performs other duties as assigned.

Qualifications
Ph.D. in a relevant discipline (e.g. Medical Informatics, Computer Science, Software Engineering, Mathematics, etc.)
Track record of publications in top journals and conferences in the field. Strong track record of quantitative and analytics mastery, and expertise in Artificial Intelligence, Machine Learning, Causal Modeling, and Knowledge Graphs. Strong coding and implementation skills. Outstanding interpersonal skills and written and verbal communication capabilities.
WORK SCHEDULE: This position may occasionally be required to work weekends and evenings. May require occasional overnight travel.

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