1

Ai Causal Inference Jobs (NOW HIRING)

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 ...

Innovate in Generative AI & Causal ML - Explore novel AI techniques, such as LLMs, reinforcement learning, and causal inference, to enhance targeting, personalization, and attribution models.

Gen AI Lead

Dallas, TX · On-site

$138K - $170K/yr

AI, Causal Inference, Time series analysis, Forecasting, Anomaly detection, Hypothesis testing, A/B testing, Git Actions, Tableau, Power BI, ThoughtSpot, Web Scraping * Data & Engineering - SQL ...

Staff Applied Scientist

Irvine, CA · On-site

$180K - $220K/yr

Innovate in Generative AI & Causal ML - Explore novel AI techniques, such as LLMs, reinforcement learning, and causal inference, to enhance targeting, personalization, and attribution models.

You will be at the forefront of designing, developing, and deploying cutting-edge Causal Inference ... AI to improve productivity and generate new insights Curious business attitude with an ability to ...

You will be at the forefront of designing, developing, and deploying cutting-edge Causal Inference ... AI to improve productivity and generate new insights Curious business attitude with an ability to ...

next page

Showing results 1-20

Ai Causal Inference information

See salary details

$16

$56

$81

How much do ai causal inference jobs pay per hour?

As of Jun 9, 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 are AI Causal Inference professionals?

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, and why are they important?

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.
Infographic showing various Ai Causal Inference job openings in the United States as of June 2026, with employment types broken down into 1% Internship, 3% Full Time, and 96% Part Time. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution, with an average salary of $118,171 per year, or $56.8 per hour.
Postdoctoral Research Position in Causal Inference

Postdoctoral Research Position in Causal Inference

Harvard University

Cambridge, MA • On-site

$75K/yr

Full-time

Posted 27 days ago


Harvard University rating

8.1

Company rating: 8.1 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

131st of 535 rated colleges and universities


Job description

Position
Details
Title
Postdoctoral Research Position in Causal Inference
School
Harvard T.H. Chan School of Public Health
Department/Area
Biostatistics
Position Description
We invite applications for a full-time Postdoctoral Research Fellow to join the causal inference team supervised by Professor Francesca Dominici. The position will focus on developing and applying novel causal inference methods for large-scale observational studies, with a particular emphasis on environmental exposures and public health. Core data resources include nationwide claims, linked with rich contextual information such as census data, weather records, and high-resolution air pollution and related environmental exposures data.
Motivated by relevant public health and policy questions, the goal is to develop methodologies for the identification, estimation, transportability, and generalization of the causal effects in complex real-world settings. Among others, methodological areas will span:
• Causal inference for spatiotemporal data,
• Methods for heterogeneous treatment effects estimation,
• Methods for multiple exposures, multiple outcomes,
• ML and AI methods for causal inference,
• Bayesian causal inference,
• methods for transportability and generalizability of causal effects across space, time, and populations.
Duties and Responsibilities
• Design, develop and implement novel causal inference methods in the areas listed in the position description.
• Work with large, high-dimensional datasets.
• Lead and contribute to manuscripts for high-impact journals (e.g., top Statistics journals and Nature-like journals).
• Present findings in internal meetings and at national/international conferences.
• Collaborate with an interdisciplinary team (bio)statisticians, data scientists, computer scientists, and climate scientists.
• Contribute to open-source code and reproducible pipelines.
Basic Qualifications
• PhD (completed or near completion) in Statistics, Biostatistics, Data Science, Computer Science or a closely related field.
• Demonstrated expertise in causal inference, with interest in methods development.
• Experience with statistical and ML methods, including at least one of the following: Bayesian methods, deep learning, spatiotemporal modeling, high-dimensional statistics.
• Proficiency in statistical programming (R and/or Python) and good practices for reproducible research.
• Experience working with large datasets and cloud computing environments.
• Excellent written and oral communication skills, with a track record of peer-reviewed publications commensurate with career stage.
• Ability to work in a collaborative, interdisciplinary environment.
Additional Qualifications
Prior experience with one or more of:
• Health claims data, EHRs, or other large-scale health/administrative datasets.
• Environmental, climate, or air pollution exposure data.
Familiarity with LLMs.
Special Instructions
Please submit the following materials:
• Cover letter describing your research interests, relevant experience, and fit for this position.
• Curriculum vitae including a list of publications.
• One to three representative publications or preprints.
Names and contact information for 2-3 references.
Contact Information
Catherine Adcock
Contact Email
catherine_adcock@harvard.edu
Salary Range
$75,000
Minimum Number of References Required
2
Maximum Number of References Allowed
3
Keywords
Causal inference; spatiotemporal modeling; generalizability; transportability; environmental health