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Causal Inference Machine Learning Postdoctoral information

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$30.7K

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$52.7K

How much do causal inference machine learning postdoctoral jobs pay per year?

As of Sep 15, 2026, the average yearly pay for causal inference machine learning postdoctoral in Humble, TX is $46,822.00, according to ZipRecruiter salary data. Most workers in this role earn between $46,200.00 and $48,800.00 per year, depending on experience, location, and employer.

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 are the key skills and qualifications needed to thrive as a causal inference machine learning postdoctoral researcher?

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 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.

Is it difficult to get a causal inference machine learning postdoctoral position?

Securing a causal inference machine learning postdoctoral position can be competitive due to specialized skills required, such as expertise in statistical methods, programming (e.g., Python or R), and a strong research background. Candidates with relevant publications, strong recommendations, and experience in machine learning frameworks often have better chances, but the availability of such positions varies by institution and funding.

What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Humble, TX look for?

The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Humble, TX are:

What cities near Humble, TX are hiring for Causal Inference Machine Learning Postdoctoral jobs?

Cities near Humble, TX with the most Causal Inference Machine Learning Postdoctoral job openings:

Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in Humble, TX as of September 2026, with employment types broken down into 89% Full Time, and 11% Contract. Highlights an 78% In-person, and 22% Remote job distribution, with an average salary of $46,822 per year, or $22.5 per hour.

Postdoctoral Fellow - Breast Surgical Oncology - Research

Houston, TX • On-site

MD Anderson
Health Care and Social Assistance • 10K+ employees
Learn more about MD Andersonexternal

$46K - $63K/yr

Full-time

Re-posted 13 days ago

MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 174 frontline employees who took The Breakroom Quiz

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 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 Apply

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