Senior Statistical Geneticist & Multi-Omics Bioinformatician

Mount Sinai Health System; New York (NY); On-site; USD 110,000 - 170,000; Full time

Job summary

Mount Sinai Health System in New York seeks a Bioinformatician III to lead analyses of large-scale genomic, proteomic, and multi-omics data within the AI and Human Health Department. You will drive analytical pipelines and contribute to publications and grants.

The role requires advanced skills in statistical genetics, software tools, and cloud/HPC environments; collaboration across clinicians and computational scientists is essential.

Qualifications

• M.S. in Biological Sciences, Bioinformatics, Computer Sciences, Statistics or related discipline; PhD preferred.
• Six years minimum experience required.
• Working experience with genetics or statistics analysis software and online resources.

Responsibilities

• Design and implement large-scale genetic analyses and QC pipelines.
• Develop reproducible computational pipelines for sequencing and multi-omics analyses.
• Integrate multi-omics with clinical data to identify disease mechanisms and targets.
• Conduct longitudinal analyses of clinical and genomic data.
• Apply ML/AI and LLM-based approaches to genomic and EHR data.
• Lead independent research projects and contribute to grants/publications.
• Collaborate with multidisciplinary teams and mentor junior analysts.

Skills

R

Python

Unix/Linux

Bash scripting

Git/GitHub

HPC

Cloud computing

Statistical genetics

Education

MS in Biological Sciences

MS in Bioinformatics

MS in Computer Sciences

MS in Statistics

PhD preferred

Tools

REGENIE

PLINK

SAIGE/SAIGE-GENE+

BOLT-LMM

GATK

bcftools/vcftools

FINEMAP/SuSiE

coloc

PRSice/LDpred/PRS-CS

Job description

Mount Sinai Health System in New York seeks a Bioinformatician III to lead analyses of large-scale genomic, proteomic, and multi-omics data within the AI and Human Health Department. You will drive analytical pipelines and contribute to publications and grants.

The role requires advanced skills in statistical genetics, software tools, and cloud/HPC environments; collaboration across clinicians and computational scientists is essential.