Post Doctoral Fellow-MSH-76880-013

Mount Sinai; United States; On-site; USD 60,000 - 70,000; Full time

Job summary

Mount Sinai in New York invites applications for a Postdoctoral Fellow in AI, causal inference, and health data science. The fellow will lead studies, develop algorithms, and analyze complex clinical data, collaborating with clinicians and interdisciplinary teams.

Candidates should have a PhD in a quantitative field, strong ML experience, and proficiency in Python or R, with excellent communication and mentoring abilities. This is a full‑time on‑site role in New York.

Qualifications

• PhD in statistics, biostatistics, CS, data science, or related field.
• Strong ML background with interest in causal inference.
• Proficiency in R and/or Python; able to handle large datasets.
• Excellent communication and collaboration skills.

Responsibilities

• Design studies and develop novel algorithms for health data science.
• Analyze large, complex datasets and validate models.
• Contribute to high‑impact publications and conference presentations.
• Mentor and train junior researchers in a multidisciplinary team.

Skills

ML

Causal inference

Statistical modeling

Communication skills

Mentoring/teaching

Education

PhD in statistics/biostatistics/computer science/data science/bioinformatics/related field

Tools

Python

R

Job description

Postdoctoral Fellow in AI, Causal Inference, and Health Data Science
Suarez-Farinas Lab – Icahn School of Medicine at Mount Sinai

About the Institution

The Icahn School of Medicine at Mount Sinai is a globally recognized leader in medical education, scientific research, and innovative patient care. As the academic hub of the Mount Sinai Health System, it spans eight hospital campuses and includes a distinguished faculty of over 5,000 members. The institution is known for its pioneering spirit, investing in transformative technologies and fostering collaborative, multidisciplinary research to advance biomedical science and improve patient outcomes.

About the Lab and Role

The Suarez‑Farinas Lab is seeking a highly motivated postdoctoral fellow to work at the intersection of artificial intelligence, causal inference, and translational health data science. Our research develops rigorous statistical and machine learning methods to uncover disease mechanisms, identify treatment‑response biomarkers, and advance precision medicine using clinical trials, real‑world data, and multi‑omics datasets. A central focus of the lab is moving beyond purely predictive models toward causal, mechanistic, and clinically actionable insights, while building scalable and reproducible analytical pipelines.

The postdoctoral researcher will lead and contribute to cutting‑edge projects involving AI and data science in healthcare. Responsibilities include designing studies, developing novel algorithms, analyzing large and complex datasets, and collaborating closely with clinicians and interdisciplinary teams. The fellow will contribute to high‑impact publications, present at leading conferences, and mentor junior trainees.

This is a full‑time, on‑site position based in New York, United States.

• PhD in statistics, biostatistics, computer science, data science, bioinformatics, or a related quantitative field
• Strong background in ML and interest or experience in causal inference (e.g., causal ML, treatment effect estimation)
• Proficiency in R and/or Python, with experience handling large, complex datasets
• Solid understanding of statistical modeling, experimental design, and algorithm development
• Experience in biomedical or healthcare research is a plus
• Demonstrated ability to work independently and collaboratively in a multidisciplinary environment
• Strong written and oral communication skills
• Experience mentoring or teaching is desirable