ZipRecruiter

Log In

1

Causal Inference Machine Learning Postdoctoral Jobs in Connecticut

The Hartford

Senior AI Machine Learning Engineer

Hartford, CT · On-site

$123K - $162K/yr

The Hartfordis seeking aSenior AI Machine Learning Engineerwithin Employee Benefits Applied AI and ... inference, post-processing, business rules integration, and downstream consumption. Deploy and ...

next page

Showing results 1-20

All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in Connecticut

Causal Inference Machine Learning Postdoctoral information

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 are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Connecticut?

For Causal Inference Machine Learning Postdoctoral jobs in Connecticut, the most frequently searched job titles are:

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

The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Connecticut are:

What cities in Connecticut are hiring for Causal Inference Machine Learning Postdoctoral jobs?

Cities in Connecticut with the most Causal Inference Machine Learning Postdoctoral job openings:

Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in Connecticut as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 21% Part Time, 1% Temporary, and 2% Contract. Highlights an 85% Physical, 2% Hybrid, and 13% Remote job distribution.

Postdoctoral Associate Position, Yale School of Public Health and Department of Biostatistics

Yale University

New Haven, CT • On-site

$49K - $66K/yr

Full-time

Re-posted 9 days ago

Yale University rating

8.6

Company rating: 8.6 out of 10

Based on 64 frontline employees who took The Breakroom Quiz

67th of 620 rated colleges and universities

Job description

Description
We are actively recruiting a postdoctoral associate to join the Public Health Data Science and Data Equity (DSDE) research team in the School of Public Health with a primary home in the Department of Biostatistics.
The postdoctoral associate will work with Dr. Bhramar Mukherjee, PhD, the inaugural Senior Associate Dean of Public Health Data Science and Data Equity, Anna M.R. Lauder endowed Professor of Biostatistics, Professor of Chronic Disease Epidemiology, Professor of Statistics and Data Science, with a focus on developing methods and tools for the analysis of electronic health records and real-world healthcare data. There will also be opportunities for collaboration with faculty with experience in cancer and cardiovascular diseases. The position is for one year and is renewable for a second year based on satisfactory performance and progress.
The postdoctoral associate will actively participate in methodological and collaborative research as well as support writing research grants. Professional development will be an integral part of the position. This role enables postdocs to gain expertise in analysis and inference within complex, non-probability observational samples while engaging in exciting applications that harness and integrate data from various sources such as electronic health records, biobanks, registries, etc. The position will also provide a solid foundation to build a research career in academia, government, or industry.
Organization: Yale School of Public Health
Department: Biostatistics
Primary Location: New Haven, CT
Education Level: PhD
Shift: Days, 40 hours/week
Work Modality: Flexible, hybrid working arrangements could be accommodated if needed
Expected Start Date: Flexible, as early as November 1, 2025
Additional information on the Yale School of Public Health, the Department of Biostatistics and the Department of Chronic Disease Epidemiology at the School of Public Health can be found via the links below:
About Us | Yale School Of Public Health
Biostatistics | Yale School of Public Health
Chronic Disease Epidemiology Department | Yale School of Public Health
Qualifications
• Completed doctorate in Biostatistics, Statistics, Data Science, Computer Science, Bioinformatics, or a related field before the start of the appointment
• Strong oral and written communication skills, and the ability to work effectively with a wide range of constituencies in a complex and diverse setting
• Proficiency with statistical computing (e.g., R, Python or C/C++)
• Demonstrated capacity to work both collaboratively and independently
• Strong strategic, analytical, and problem-solving skills
• Strong interpersonal and project management skills to facilitate timely and professional deliverables
• Experience with causal inference, machine learning, and artificial intelligence is desirable
• Experience with clinical, EHR, or biobank data analyses is desirable
Application Instructions
To apply:
Interested individuals should submit a (1) CV, (2) cover letter that addresses their specific interest in this position, skills and experiences directly related to this position, and highlight overall research interests and plan, and (3) contact information for three professional references via Interfolio. Review of applications will begin immediately and will continue until the position is filled.
Please apply online.
Questions regarding this position should be directed to bhramar.mukherjee@yale.edu.

What Yale University employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Apply