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Full Time Causal Inference Jobs (NOW HIRING)

YO AI Labs

Data Scientist

Glendale, CA

Glendale, USA * Job Type: Full-time Must Haves: * Python Machine Learning, data science, AWS ... Robust knowledge of causal inference approaches such as propensity scores, synthetic controls ...

Shipt, Inc.

Senior Data Scientist I

Birmingham, AL · On-site +1

Shipt, Inc. seeks a full-time Senior Data Scientist I responsible for performing a variety of ... causal inference methods, including treatment effect estimation, counterfactual analysis, and ...

Summit Health

Senior Data Scientist I

Birmingham, AL

$247K/yr

Shipt, Inc. seeks a full-time Senior Data Scientist I responsible for performing a variety of ... causal inference methods, including treatment effect estimation, counterfactual analysis, and ...

Shipt

Senior Data Scientist I

Birmingham, AL

Shipt, Inc. seeks a full-time Senior Data Scientist I responsible for performing a variety of ... causal inference methods, including treatment effect estimation, counterfactual analysis, and ...

Walmart

(USA) Principal, Data Scientist

Springdale, AR · On-site

$110K - $220K/yr

... causal inference, and deployment of scalable solutions. You will work closely with agentic AI teams ... full-time and part-time associates in Walmart and Sam's Club facilities. Programs range from high ...

Walmart

(USA) Principal, Data Scientist

Gravette, AR · On-site

$110K - $220K/yr

... causal inference, and deployment of scalable solutions. You will work closely with agentic AI teams ... full-time and part-time associates in Walmart and Sam's Club facilities. Programs range from high ...

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Full Time Causal Inference information

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How much do full time causal inference jobs pay per year?

As of Aug 17, 2026, the average yearly pay for full time causal inference in the United States is $96,278.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $98,500.00 per year, depending on experience, location, and employer.
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Full Time Causal Inference jobs near you
Infographic showing various Full Time Causal Inference job openings in the United States as of August 2026, with employment types broken down into 82% Full Time, 16% Part Time, and 2% Contract. Highlights an 71% Physical, 3% Hybrid, and 26% Remote job distribution, with an average salary of $96,278 per year, or $46.3 per hour.

Postdoctoral Research Position in Causal Inference

Harvard University

Cambridge, MA • On-site

$75K/yr

Full-time

Re-posted 6 days ago

Harvard University rating

8.5

Company rating: 8.5 out of 10

Based on 12 frontline employees who took The Breakroom Quiz

80th of 618 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

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