... methods, causal inference, machine learning, pragmatic trial designs with cluster-randomization ... 1+ years of postdoctoral experience. The ideal candidates will be outstanding early-career ...
... methods, causal inference, machine learning, pragmatic trial designs with cluster-randomization ... 1+ years of postdoctoral experience. The ideal candidates will be outstanding early-career ...
Responsibilities may include : (1) development and application of statistical and machine learning ... Eric Tchetgen Tchetgen, with a focus on methodological developments for Causal Inference and ...
Responsibilities may include : (1) development and application of statistical and machine learning ... Eric Tchetgen Tchetgen, with a focus on methodological developments for Causal Inference and ...
... statistical, causal, and machine learning methods for analyzing complex longitudinal data ... inference applications. The successful applicant will have an opportunity to work under the ...
... statistical, causal, and machine learning methods for analyzing complex longitudinal data ... inference applications. The successful applicant will have an opportunity to work under the ...
This role is heavily focused on econometric modeling, time series analysis, and causal inference ... Experience with machine learning models (classification, regression, tree-based models, etc.
This role is heavily focused on econometric modeling, time series analysis, and causal inference ... Experience with machine learning models (classification, regression, tree-based models, etc.
We operate with high velocity at the intersection of machine learning, causal inference, functional genomics, spatial biology, and real-world clinical data; your expertise, execution, technical ...
We operate with high velocity at the intersection of machine learning, causal inference, functional genomics, spatial biology, and real-world clinical data; your expertise, execution, technical ...
We operate with high velocity at the intersection of machine learning, causal inference, functional genomics, spatial biology, and real-world clinical data; your expertise, execution, technical ...
We operate with high velocity at the intersection of machine learning, causal inference, functional genomics, spatial biology, and real-world clinical data; your expertise, execution, technical ...
Principal Scientist, Oncology Data Science (Translational Science)
Upper Providence, PA · On-site
$121 - $202/hr
We operate with high velocity at the intersection of machine learning, causal inference, functional genomics, spatial biology, and real-world clinical data; your expertise, execution, technical ...
Principal Scientist, Oncology Data Science (Translational Science)
Upper Providence, PA · On-site
$121 - $202/hr
We operate with high velocity at the intersection of machine learning, causal inference, functional genomics, spatial biology, and real-world clinical data; your expertise, execution, technical ...
Senior Biostatistician
Conshohocken, PA · Hybrid
$150K - $182K/yr
... and in causal inference using real-world data - and contributes to biomarker development ... Develop and apply statistical and machine-learning models to high-dimensional data to support ...
Senior Biostatistician
Conshohocken, PA · Hybrid
$150K - $182K/yr
... and in causal inference using real-world data - and contributes to biomarker development ... Develop and apply statistical and machine-learning models to high-dimensional data to support ...
Senior Biostatistician
Conshohocken, PA · On-site
$150K - $182K/yr
... and in causal inference using real-world data - and contributes to biomarker development ... Develop and apply statistical and machine-learning models to high-dimensional data to support ...
Senior Biostatistician
Conshohocken, PA · On-site
$150K - $182K/yr
... and in causal inference using real-world data - and contributes to biomarker development ... Develop and apply statistical and machine-learning models to high-dimensional data to support ...
Applied AI Engineer
Upper Providence, PA · On-site
We are building a future where state-of-the-art software, AI, and machine learning enable us to ... Experience with knowledge graph construction, causal inference, or large perturbation models.
Applied AI Engineer
Upper Providence, PA · On-site
We are building a future where state-of-the-art software, AI, and machine learning enable us to ... Experience with knowledge graph construction, causal inference, or large perturbation models.
Machine Learning Engineer- Inference Optimization | Experienced Hire
Bala Cynwyd, PA · On-site
$110 - $150/hr
Overview We are looking for a Machine Learning Engineer focused on low-latency inference optimization to help build, tune, and productionize high-performance model serving systems. This role sits at ...
Machine Learning Engineer- Inference Optimization | Experienced Hire
Bala Cynwyd, PA · On-site
$110 - $150/hr
Overview We are looking for a Machine Learning Engineer focused on low-latency inference optimization to help build, tune, and productionize high-performance model serving systems. This role sits at ...
Applied AI Engineer
Upper Providence, PA · On-site
We are building a future where state-of-the-art software, AI, and machine learning enable us to ... Experience with knowledge graph construction, causal inference, or large perturbation models.
Applied AI Engineer
Upper Providence, PA · On-site
We are building a future where state-of-the-art software, AI, and machine learning enable us to ... Experience with knowledge graph construction, causal inference, or large perturbation models.
Biostatistician
West Chester, PA · On-site
$70 - $98/hr
... causal inference. 7. Min 4 yrs experience with advanced statistical models such as mixed effect model approaches for repeated measures, Machine Learning (ML) methods. Position Summary: * Provide ...
Quick apply
Biostatistician
West Chester, PA · On-site
$70 - $98/hr
... causal inference. 7. Min 4 yrs experience with advanced statistical models such as mixed effect model approaches for repeated measures, Machine Learning (ML) methods. Position Summary: * Provide ...
Apply data science, machine learning, statistical modeling, and quantitative methodologies to ... Establish and oversee rigorous experimentation, causal inference, and measurement frameworks to ...
Apply data science, machine learning, statistical modeling, and quantitative methodologies to ... Establish and oversee rigorous experimentation, causal inference, and measurement frameworks to ...
Apply data science, machine learning, statistical modeling, and quantitative methodologies to ... Establish and oversee rigorous experimentation, causal inference, and measurement frameworks to ...
Apply data science, machine learning, statistical modeling, and quantitative methodologies to ... Establish and oversee rigorous experimentation, causal inference, and measurement frameworks to ...
Apply data science, machine learning, statistical modeling, and quantitative methodologies to ... Establish and oversee rigorous experimentation, causal inference, and measurement frameworks to ...
Apply data science, machine learning, statistical modeling, and quantitative methodologies to ... Establish and oversee rigorous experimentation, causal inference, and measurement frameworks to ...
Apply data science, machine learning, statistical modeling, and quantitative methodologies to ... Establish and oversee rigorous experimentation, causal inference, and measurement frameworks to ...
Apply data science, machine learning, statistical modeling, and quantitative methodologies to ... Establish and oversee rigorous experimentation, causal inference, and measurement frameworks to ...
Apply data science, machine learning, statistical modeling, and quantitative methodologies to ... Establish and oversee rigorous experimentation, causal inference, and measurement frameworks to ...
Apply data science, machine learning, statistical modeling, and quantitative methodologies to ... Establish and oversee rigorous experimentation, causal inference, and measurement frameworks to ...
Apply data science, machine learning, statistical modeling, and quantitative methodologies to ... Establish and oversee rigorous experimentation, causal inference, and measurement frameworks to ...
Quick apply
Apply data science, machine learning, statistical modeling, and quantitative methodologies to ... Establish and oversee rigorous experimentation, causal inference, and measurement frameworks to ...
Apply data science, machine learning, statistical modeling, and quantitative methodologies to ... Establish and oversee rigorous experimentation, causal inference, and measurement frameworks to ...
Quick apply
Apply data science, machine learning, statistical modeling, and quantitative methodologies to ... Establish and oversee rigorous experimentation, causal inference, and measurement frameworks to ...
Causal Inference Machine Learning Postdoctoral information
See Media, PA salary details
$35.3K - $37.6K
6% of jobs
$37.6K - $39.9K
0% of jobs
$39.9K - $42.2K
0% of jobs
$42.2K - $44.6K
0% of jobs
$44.6K - $46.9K
1% of jobs
$46.9K - $49.2K
4% of jobs
$49.2K - $51.5K
9% of jobs
$52.3K is the 25th percentile. Wages below this are outliers.
$51.5K - $53.8K
11% of jobs
The median wage is $54.8K / yr.
$53.8K - $56.1K
42% of jobs
$56.2K is the 75th percentile. Wages above this are outliers.
$56.1K - $58.4K
21% of jobs
$58.4K - $60.7K
5% of jobs
$35.3K
$54K
$60.7K
How much do causal inference machine learning postdoctoral jobs pay per year?
As of Aug 25, 2026, the average yearly pay for causal inference machine learning postdoctoral in Media, PA is $53,956.00, according to ZipRecruiter salary data. Most workers in this role earn between $53,200.00 and $56,200.00 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?
| Aspect | Causal Inference Machine Learning Postdoctoral | Data Scientist |
|---|---|---|
| Required Credentials | PhD in statistics, machine learning, or related field | Bachelor's or Master's in data science, computer science, or related field |
| Work Environment | Academic research, research labs, universities | Corporate, tech companies, startups |
| Industry Usage | Research, academia, specialized industry projects | Business analytics, product development, data-driven decision making |
| Common Search/Comparison | Yes | Yes |
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 cities near Media, PA are hiring for Causal Inference Machine Learning Postdoctoral jobs?
Cities near Media, PA with the most Causal Inference Machine Learning Postdoctoral job openings:
Assistant Professor of Epidemiology, Research Track
Philadelphia, PA • On-site
University of Pennsylvania •
Colleges, Universities, and Professional Schools • 10K+ employees10,000+ employees
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Re-posted 11 days ago
Job description
Description
The Department of Biostatistics and Epidemiology at the Perelman School of Medicine at the University of Pennsylvania seeks candidates for several Assistant Professor positions in the non-tenure research track. Expertise is required in the specific area of the statistical design and analysis of randomized clinical trials. Applicants must have a Ph.D. or equivalent degree.
Additional qualifications include:
• A strong background and expertise in Bayesian statistical methods, causal inference, machine learning, pragmatic trial designs with cluster-randomization methods, and complex missing data methodology.
• Expertise in statistical programming (R, Python, Stan/BUGS).
• Proficiency with clinical trial simulation to support both methodological research and the initial design and subsequent modification of trials.
• Experience supporting large-scale trials, clinical research networks, and/or data and safety monitoring board statistical activities.
• Experience collaborating with clinician-scientists, particularly physician-scientists.
• Proficiency in analyzing and interpreting patient-reported outcome measures such as quality-of-life endpoints, including handling of missing data such as that due to non-response, death, or other intercurrent events.
• Demonstrated aptitude working with state-of-the-art computing infrastructure, Overleaf/LaTeX, GitHub, and supporting reproducible research pipelines.
Research or scholarship responsibilities may include demonstrated ability to lead peer-reviewed publications in clinical trials methodology and support multi-site collaborative research projects.
The Center for Clinical Trials Innovation in the Division of Epidemiology, Department of Biostatistics, Epidemiology, and Informatics, in collaboration with the Palliative and Advanced Illness Research Center, seeks candidates with a PhD in Statistical Epidemiology, Biostatistics, Statistics, or a closely related quantitative field, with 1+ years of postdoctoral experience. The ideal candidates will be outstanding early-career researchers who will advance innovative clinical trial methodologies and lead cutting-edge research in the statistical design, analysis, monitoring, and interpretation of complex multi-arm, cluster, pragmatic, Bayesian, adaptive, and platform randomized trials. These faculty will be expected to lead and publish high-impact research in top-tier biostatistics, clinical trial, and clinical research journals; support, prepare, and submit grant applications; and support ongoing randomized trials and trial methodology awards with Penn faculty and external partners. In these roles, they will serve as lead biostatisticians on multi-center randomized clinical trials and methodology projects and grants, develop and evaluate composite outcome measures and interpretation frameworks, apply causal inference methods to augment experimental data interpretation, and provide independent statistical expertise and leadership to research teams.
The Department of Biostatistics and Epidemiology at the Perelman School of Medicine at the University of Pennsylvania seeks candidates for several Assistant Professor positions in the non-tenure research track. Expertise is required in the specific area of the statistical design and analysis of randomized clinical trials. Applicants must have a Ph.D. or equivalent degree.
Additional qualifications include:
• A strong background and expertise in Bayesian statistical methods, causal inference, machine learning, pragmatic trial designs with cluster-randomization methods, and complex missing data methodology.
• Expertise in statistical programming (R, Python, Stan/BUGS).
• Proficiency with clinical trial simulation to support both methodological research and the initial design and subsequent modification of trials.
• Experience supporting large-scale trials, clinical research networks, and/or data and safety monitoring board statistical activities.
• Experience collaborating with clinician-scientists, particularly physician-scientists.
• Proficiency in analyzing and interpreting patient-reported outcome measures such as quality-of-life endpoints, including handling of missing data such as that due to non-response, death, or other intercurrent events.
• Demonstrated aptitude working with state-of-the-art computing infrastructure, Overleaf/LaTeX, GitHub, and supporting reproducible research pipelines.
Research or scholarship responsibilities may include demonstrated ability to lead peer-reviewed publications in clinical trials methodology and support multi-site collaborative research projects.
The Center for Clinical Trials Innovation in the Division of Epidemiology, Department of Biostatistics, Epidemiology, and Informatics, in collaboration with the Palliative and Advanced Illness Research Center, seeks candidates with a PhD in Statistical Epidemiology, Biostatistics, Statistics, or a closely related quantitative field, with 1+ years of postdoctoral experience. The ideal candidates will be outstanding early-career researchers who will advance innovative clinical trial methodologies and lead cutting-edge research in the statistical design, analysis, monitoring, and interpretation of complex multi-arm, cluster, pragmatic, Bayesian, adaptive, and platform randomized trials. These faculty will be expected to lead and publish high-impact research in top-tier biostatistics, clinical trial, and clinical research journals; support, prepare, and submit grant applications; and support ongoing randomized trials and trial methodology awards with Penn faculty and external partners. In these roles, they will serve as lead biostatisticians on multi-center randomized clinical trials and methodology projects and grants, develop and evaluate composite outcome measures and interpretation frameworks, apply causal inference methods to augment experimental data interpretation, and provide independent statistical expertise and leadership to research teams.
About University of Pennsylvania
Sourced by ZipRecruiter
The University of Pennsylvania, the largest private employer in Philadelphia, is a world-renowned leader in education, research, and innovation. This historic, Ivy League school consistently ranks among the top 10 universities in the annual U.S. News & World Report survey. Penn has 12 highly-regarded schools that provide opportunities for undergraduate, graduate and continuing education, all influenced by Penn's distinctive interdisciplinary approach to scholarship and learning. As an employer Penn has been ranked nationally on many occasions with the most recent award from Forbes who named Penn one of America's Best Employers By State in 2021.
Industry
Colleges, universities, and professional schools
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10,000+ Employees
Headquarters location
Philadelphia, PA, US
Year founded
1740
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