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Causal Inference Machine Learning Postdoctoral Jobs in New York, NY

Mount Sinai Hospital

Postdoctoral Fellow-MSH

Manhattan, NY · On-site

$53K - $73K/yr

... machine-learning/deep-learning methodology research with application to biomedical data. • ... and causal-inference methodology research with application to medical/clinical-trial studies. The ...

Zocdoc

Director, Marketing Data Science

New York, NY · On-site

You follow how the field is evolving (privacy changes, signal loss, new causal inference approaches ... Machine Learning, Artificial Intelligence, Economics, Physics, or a related field is required. A ...

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Causal Inference Machine Learning Postdoctoral information

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$66.7K

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

As of Aug 10, 2026, the average yearly pay for causal inference machine learning postdoctoral in New York, NY is $59,322.00, according to ZipRecruiter salary data. Most workers in this role earn between $58,500.00 and $61,800.00 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.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in New York, NY? For Causal Inference Machine Learning Postdoctoral jobs in New York, NY, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in New York, NY look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in New York, NY are:
What cities near New York, NY are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities near New York, NY with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you

Postdoctoral Fellow-MSH

Mount Sinai Hospital

Manhattan, NY • On-site

$53K - $73K/yr

Full-time

Re-posted 23 days ago

Mount Sinai rating

7.7

Company rating: 7.7 out of 10

Based on 294 frontline employees who took The Breakroom Quiz

158th of 887 rated healthcare providers

Job description


Roles & Responsibilities:
At the Icahn Institute, our vision is to transform biomedical research and healthcare delivery into a data-driven, evidence-based, patient-tailored discipline. The Icahn Institute was founded in 2011 to help advance precision medicine with cutting-edge technologies, novel partnerships between the public and private sectors, and world class computational and analytical resources. By maximally leveraging information from patients around the world, we deliver premier precision care optimized for each patient, while discovering breakthrough, next-generation treatments through insights derived from cutting-edge analytics applied to unprecedented amounts of patient-derived data. We promote a core set of values - designed to promote our future vision of team-oriented, data driven global biomedical research: 1) Do good for the patient, 2) Simplify, 3) Share openly, 4) Focus, 5) Synergize, 6) Contributions, not politics, and 7) Deliver.
Postdoc fellows may work on many of the cutting-edge research areas at Mount Sinai, including:
• Analyzing high dimensional molecular profiles including but not constraint to DNA/RNA sequencing, epigenetic, proteomics, and microbiome data;
• Analyzing massive electronic medical record data;
• Analyzing irregular time course data from mobile health studies;
• Scalable machine-learning/deep-learning methodology research with application to biomedical data.
• Mediation and causal-inference methodology research with application to medical/clinical-trial studies.
The ideal candidate will have strong analytical and programming proficiencies and proven expertise in high-dimensional data analysis.
• Develop and use computational techniques to analyze large-scale biomedical datasets.
- Develop and implement proper statistical/computational models;
- Develop and implement proper data visualization tools;
- Perform rigorous and reproducible data analyses;
- Identify and resolve technical issues and propose upgrades to current software;
• Take the lead in manuscripts preparation and facilitate grant writing;
• Collaborate with researches from other disciplines;
• Participate scientific conferences and present research works;
• Other technical and leadership duties as assigned and commensurate with experience and skills.
Requirements:
• Ph.D. in Computer Science, Bioinformatics, Statistics, Computational Biology, or other relevant quantitative discipline.
• Good organization and communication skills, with demonstrated ability to productively work as a member of a team.
• Demonstrate outstanding scientific writing skills and experience.
• Experience in data science, machine learning, statistical modeling, genomic analysis, or other relevant analytical field
• Experience in programming environments such as R, Matlab, SAS, Stata, Python/Jupyter
• Working experience with genetics or statistics analysis and online resources or other biomedical data analysis is a plus
About Us
Strength through Unity and Inclusion
The Mount Sinai Health System is committed to fostering an environment where everyone can contribute to excellence. We share a common dedication to delivering outstanding patient care. When you join us, you become part of Mount Sinai's unparalleled legacy of achievement, education, and innovation as we work together to transform healthcare. We encourage all team members to actively participate in creating a culture that ensures fair access to opportunities, promotes inclusive practices, and supports the success of every individual.
At Mount Sinai, our leaders are committed to fostering a workplace where all employees feel valued, respected, and empowered to grow. We strive to create an environment where collaboration, fairness, and continuous learning drive positive change, improving the well-being of our staff, patients, and organization. Our leaders are expected to challenge outdated practices, promote a culture of respect, and work toward meaningful improvements that enhance patient care and workplace experiences. We are dedicated to building a supportive and welcoming environment where everyone has the opportunity to thrive and advance professionally. Explore this opportunity and be part of the next chapter in our history.
About the Mount Sinai Health System:
Mount Sinai Health System is one of the largest academic medical systems in the New York metro area, with more than 48,000 employees working across eight hospitals, more than 400 outpatient practices, more than 300 labs, a school of nursing, and a leading school of medicine and graduate education. Mount Sinai advances health for all people, everywhere, by taking on the most complex health care challenges of our time - discovering and applying new scientific learning and knowledge; developing safer, more effective treatments; educating the next generation of medical leaders and innovators; and supporting local communities by delivering high-quality care to all who need it. Through the integration of its hospitals, labs, and schools, Mount Sinai offers comprehensive health care solutions from birth through geriatrics, leveraging innovative approaches such as artificial intelligence and informatics while keeping patients' medical and emotional needs at the center of all treatment. The Health System includes more than 9,000 primary and specialty care physicians; 13 joint-venture outpatient surgery centers throughout the five boroughs of New York City, Westchester, Long Island, and Florida; and more than 30 affiliated community health centers. We are consistently ranked by U.S. News & World Report's Best Hospitals, receiving high "Honor Roll" status, and are highly ranked: No. 1 in Geriatrics, top 5 in Cardiology/Heart Surgery, and top 20 in Diabetes/Endocrinology, Gastroenterology/GI Surgery, Neurology/Neurosurgery, Orthopedics, Pulmonology/Lung Surgery, Rehabilitation, and Urology. New York Eye and Ear Infirmary of Mount Sinai is ranked No. 12 in Ophthalmology. U.S. News & World Report's "Best Children's Hospitals" ranks Mount Sinai Kravis Children's Hospital among the country's best in several pediatric specialties. The Icahn School of Medicine at Mount Sinai is ranked No. 11 nationwide in National Institutes of Health funding and in the 99th percentile in research dollars per investigator according to the Association of American Medical Colleges. Newsweek's "The World's Best Smart Hospitals" ranks The Mount Sinai Hospital as No. 1 in New York and in the top five globally, and Mount Sinai Morningside in the top 20 globally.
Equal Opportunity Employer
The Mount Sinai Health System is an equal opportunity employer, complying with all applicable federal civil rights laws. We do not discriminate, exclude, or treat individuals differently based on race, color, national origin, age, religion, disability, sex, sexual orientation, gender, veteran status, or any other characteristic protected by law. We are deeply committed to fostering an environment where all faculty, staff, students, trainees, patients, visitors, and the communities we serve feel respected and supported. Our goal is to create a healthcare and learning institution that actively works to remove barriers, address challenges, and promote fairness in all aspects of our organization.

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