Postdoc: Data Fusion & Federated Mediation Analytics

Statistics Interest Group; New York (NY); On-site; USD 60,000 - 70,000; Full time

Job summary

Memorial Sloan Kettering Cancer Center in New York invites applications for a postdoctoral fellow in biostatistics and epidemiology. The role focuses on developing and applying novel statistical methods for data fusion, transfer, and federated learning across large healthcare datasets, including electronic health records and clinical trial data.

Under the mentorship of Dr. Andriy Derkach, you will work on mediation analysis with high‑dimensional mediators, bridging biostatistics, data science,

Qualifications

• PhD degree in biostatistics, statistics, bioinformatics, or a related field.
• Experience in methodology in the areas of missing data, public health studies under various sampling designs, high-dimensional data or causal inference is desired but not required
• Solid methodological training in statistics; be comfortable with large data sets; proficient in at least one of R/Matlab/Python; experience on Unix/Linux and shell scripting

Responsibilities

• Develop and apply novel statistical methods for data fusion, transfer, and federated learning across large healthcare datasets.
• Collaborate to address challenges in mediation analysis using multi-source data.
• Work with a multidisciplinary research team under mentoring from Dr. Andriy Derkach.

Skills

Methodology

Causal inference

Large data handling

Education

PhD in biostatistics / statistics / bioinformatics or related field

Tools

R

Python

Matlab

Unix/Linux

Shell scripting

Job description

Memorial Sloan Kettering Cancer Center in New York invites applications for a postdoctoral fellow in biostatistics and epidemiology. The role focuses on developing and applying novel statistical methods for data fusion, transfer, and federated learning across large healthcare datasets, including electronic health records and clinical trial data.

Under the mentorship of Dr. Andriy Derkach, you will work on mediation analysis with high‑dimensional mediators, bridging biostatistics, data science,