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Causal Inference Machine Learning Postdoctoral Jobs in Connecticut

Socket.dev

Machine Learning Engineer

Shelton, CT · On-site

$56 - $74/hr

Design and implement the compute and orchestration for training and inference workloads on AWS (S3 ... Experience putting machine learning models or statistical analyses into production and keeping them ...

Sperry Rail, Inc.

Machine Learning Engineer

Shelton, CT · On-site

Machine Learning Engineer Sperry Rail, Inc. Shelton, Connecticut, United States About this position ... Design and implement the compute and orchestration for training and inference workloads on AWS (S3 ...

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 ...

dicedemo

$108K - $142K/yr

Experience supporting LLM training, fine-tuning, RAG, or inference workloads * Experience with MLOps platforms such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure Machine Learning * Experience ...

Block

... causal reasoning, agentic systems, and product intelligence. The goal is not simply to build ... Preference and goal inference * Learning when intervention creates value versus friction Agentic ...

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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:

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 in Pharmacoepidemiology, Perinatal Epidemiology, and Causal Inferenc

New Haven, CT • On-site

Yale University
Colleges, Universities, and Professional Schools • 10K+ employees
Learn more about Yale Universityexternal

Full-time

Re-posted 4 days ago

Yale University rating

8.2

Company rating: 8.2 out of 10

Based on 65 frontline employees who took The Breakroom Quiz

Job description

Description
Posting date: November 15th, 2025
Title of the Position: Postdoctoral Associate
School: Yale School of Public Health
Department: Department of Environmental Health Sciences, Yale Center for Perinatal, Pediatric and Environmental Epidemiology
Anticipated Appointment Date: February 15th, 2026
Fixed Term: One year, Full-time (renewable)
The Liew lab at the Yale School of Public Health (YSPH) and the Yale Center for Perinatal, Pediatric, and Environmental Epidemiology (CPPEE) is inviting qualified individuals to apply for a Postdoctoral Associate position in the multidisciplinary fields of pharmacoepidemiology, perinatal epidemiology, and causal inference, starting on February 15th, 2026, or soon thereafter. The position is a one-year, full-time role, renewable contingent upon performance and funding. The position is supported by a Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) project. The Postdoctoral Associate will collaborate with a renowned national and international team to develop cutting-edge epidemiologic methods and investigate the heterogeneity of medication effects during pregnancy and child development. The Postdoctoral Associate will have opportunities to work with the Danish National Birth Cohort (DNBC) in Denmark, as well as with US MarketScan data and Yale electronic healthcare data. The research partners include the Yale Pharmacoepidemiology Working Group (Yale PEW) and the Practical Causal Inference (PCI) lab at UCLA.
Qualifications
Candidates should possess a PhD, preferably in epidemiology, biostatistics, and/or health data sciences. Candidates with research experience analyzing longitudinal cohort or health registry data using causal inference methods, as well as those with research interests relevant to perinatal or pharmacoepidemiology, are preferred.
The position will require extensive experience with programming languages and statistical software packages, such as R or SAS. The Postdoctoral Associate position will require excellent interpersonal skills to facilitate effective communications and collaborations across local, national, and international research teams. The Postdoctoral Associate position requires motivation to work in an inclusive team-oriented environment, strong scientific integrity, and a high level of competency in English, both in written and oral formats.
Salary will be based on the Yale Postdoctoral Compensation policy, and support for travel to conferences may also be available.
Application Instructions
Yale University will use Interfolio to search for this position. Applicants receive a free Dossier account and can send all application materials at no cost.
Please apply online.
To apply, please submit a CV, a cover letter, and contact information for three references. In the cover letter to the application package, please summarize your relevant research experience, indicate your specific research interests, and the date you will be available to start.
For questions, please contact the Yale Center for Perinatal, Pediatric and Environmental Epidemiology, Email: cppee@yale.edu

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