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Causal Inference Jobs (NOW HIRING)

Apple

Applied ML Science Manager

Cupertino, CA

$198.30K - $342.80K/yr

You will be at the forefront of designing, developing, and deploying cutting-edge Causal Inference solutions, that directly impact our products and provide a granular understanding of key business ...

Apple

Applied Scientist

Austin, TX · On-site

We are currently seeking an experienced and passionate Applied Scientist, who will work on innovative products at the intersection of causal inference, statistics, and machine learning to help ...

PandaDoc

Staff GTM Data Scientist

Causal Inference Beyond A/B: Apply advanced causal inference techniques (e.g., difference-in-differences, synthetic control, propensity score matching, and instrumental variables) to scenarios where ...

Acunor

Data Scientist - TIme Series

Philadelphia, PA · Hybrid

This role focuses on forecasting, causal inference, customer behavior analytics, and statistical modeling using large-scale datasets. The ideal candidate will have deep hands-on experience with ...

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How much do causal inference jobs pay per year?

As of May 29, 2026, the average yearly pay for causal inference in the United States is $99,231.00, according to ZipRecruiter salary data. Most workers in this role earn between $86,000.00 and $108,500.00 per year, depending on experience, location, and employer.

What is a Causal Inference job?

A Causal Inference job involves using statistical and computational methods to determine cause-and-effect relationships from data. Professionals in this field work with observational and experimental data to identify causal impacts, often in domains like economics, healthcare, social sciences, and technology. They apply techniques such as propensity score matching, instrumental variables, and difference-in-differences to ensure rigorous analysis. These roles are commonly found in academia, policy research, and data science teams within tech and finance companies. Strong skills in statistics, programming (e.g., Python, R), and experimental design are typically required.

What are the key skills and qualifications needed to thrive in the Causal Inference position, and why are they important?

Success in a Causal Inference role requires strong statistical knowledge, expertise in experimental and quasi-experimental methodologies, and advanced proficiency in programming languages like R or Python, typically acquired with an advanced degree in statistics, economics, data science, or a related field. Familiarity with specialized statistical software (such as Stata, SAS, or causal inference packages in R/Python), as well as experience with large datasets and machine learning tools, is highly valued. Excellent problem-solving abilities, clear communication, and collaboration skills are essential soft skills for effectively conveying complex findings to diverse teams. These competencies are critical to producing reliable insights that guide evidence-based decision-making in business, healthcare, or policy settings.

What are some common challenges faced in a Causal Inference position?

Professionals in Causal Inference often encounter challenges such as dealing with confounding factors, addressing selection bias, and ensuring the validity of assumptions behind statistical models. They must carefully design experiments or leverage observational data while staying vigilant about potential data quality issues and model limitations. Collaboration with subject matter experts, data engineers, and business stakeholders is common to ensure accurate contextualization of results. Overcoming these challenges requires a mix of technical acumen and strong communication skills to translate complex analyses into actionable recommendations.
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Causal Inference jobs near you
Infographic showing various Causal Inference job openings in the United States as of May 2026, with employment types broken down into 1% Internship, 95% Full Time, 3% Part Time, and 1% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $99,231 per year, or $47.7 per hour.
Postdoctoral Fellow - Breast Surgical Oncology - Research

Postdoctoral Fellow - Breast Surgical Oncology - Research

MD Anderson Cancer Center

Houston, TX • On-site

$64K - $76K/yr

Full-time

Medical, Dental, Retirement, PTO

Posted 24 days ago

MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 163 frontline employees who took The Breakroom Quiz

32nd of 864 rated healthcare providers

Job description

The postdoctoral fellow will work on an externally funded methods development project focused on causal inference for long-term pharmacotherapy. The central methodological contribution is a framework for "patient-choice (PC) protocols": a class of treatment strategies that allow patients to flexibly balance quality of life and clinical outcomes during sustained pharmacotherapy. The applied context is adjuvant endocrine therapy for patients with breast cancer. The fellow will contribute to theoretical development, estimation, and real-world application of these methods across large healthcare claims databases (Merative MarketScan, SEER-Medicare) and data from an international phase-III clinical trial (PALLAS).
Under the general guidance of the Principal Investigator, the postdoctoral fellow will:
• Develop and formalize causal estimands using counterfactual theory, causal directed acyclic graphs, and single world intervention graphs.
• Derive efficient influence functions and develop targeted minimum loss-based estimation (TMLE) algorithms for a general class of PC protocols, including extensions to dynamic regime marginal structural models.
• Implement doubly robust, semiparametrically efficient estimators that incorporate flexible machine learning algorithms for confounding control in high-dimensional longitudinal data.
• Develop and apply sensitivity analysis methods.
• Analyze large-scale observational claims data and contribute to open-source software development in R.
• Prepare manuscripts and present at national and international conferences.
All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.
LEARNING OBJECTIVES
• The fellow will develop expertise in a novel class of causal estimands for longitudinal treatment strategies with non-adherence, building from foundational identification theory through to efficient nonparametric estimation and open-source implementation.
• The fellow will gain hands-on experience applying causal inference methods in large-scale healthcare claims data.
• The postdoctoral fellow will participate in a weekly causal inference conference with opportunities to collaborate on methods development and applied projects in causal inference research.
• The fellow will develop an independent publication record through authorship on methods and applied manuscripts, and will have opportunities to present work and lead workshops at major statistical and epidemiologic conferences.
ELIGIBILITY REQUIREMENTS
• Ph.D. in Biostatistics, Statistics, or Epidemiology with a strong focus on causal inference methods
• Strong programming skills in R and SAS required, with experience using large administrative claims datasets
POSITION INFORMATION
MD Anderson offers full-time postdoc positions with a salary ranging from $64,000 to $76,000 . depending on the number of years of postgraduate experience. The University of Texas MD Anderson Cancer Center offers excellent benefits , including medical, dental, paid time off , retirement , tuition benefits, educational opportunities, and individual and team recognition
Offsite work arrangements are subject to approval and may be modified or revoked at any time based on business needs, performance considerations, or regulatory requirements.
This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html

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