Bioinformatician III - Windreich Department of AI & Human Health Research

We are seeking a highly motivated genetic epidemiologist/statistical geneticist to join a growing genomics and precision medicine research program at the Artificial Intelligence and Human Health Department as Bioinformatician III . The successful candidate will lead and support analyses of large-scale genomic, proteomic, multi-omics, and longitudinal clinical datasets, applying advanced statistical genetics, bioinformatics, machine learning, and AI approaches to uncover the biological basis of complex disease and rare disorders. This role offers the opportunity to drive independent research projects, contribute to high-impact publications and grants, and collaborate closely with a multidisciplinary team at the forefront of human genetics and precision medicine.

Responsibilities

• Design and implement large-scale genetic analyses , including QC pipelines, GWAS, rare variant association analyses, PRS construction, fine-mapping, proteomics, and integrative genomics.
• Develop reproducible computational pipelines for sequencing, association testing, multi-omics integration, and downstream biological interpretation.
• Integrate multi-omics data, clinical, and imaging data to identify disease mechanisms, characterize disease heterogeneity, uncover risk factors, and prioritize therapeutic targets.
• Conduct longitudinal analyses of clinical and genomic data, including time-to-event analyses, disease progression modeling, and trajectory analyses.
• Apply machine learning and LLM-based approaches to large-scale genomic, multi-omics, imaging, and EHR data for gene discovery, disease subtyping, and precision medicine research.
• Lead independent research projects and contribute to collaborative studies from conception through publication.
• Contribute to study design, grant applications, manuscripts, presentations, and scientific reporting.
• Mentor trainees and junior analysts and help establish best practices for data management, reproducibility, and computational workflows.
• Collaborate closely with investigators, clinicians, and computational scientists across disciplines.
• Other duties as assigned.

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.

Preferred :

· Proficiency in R , Python , Unix/Linux, Bash scripting Git/GitHub, high-performance computing, and cloud computing environments.

· Strong experience with statistical genetics analysis workflows and tools including REGENIE, PLINK, SAIGE/SAIGE-GENE+, BOLT-LMM, GATK, bcftools/vcftools, FINEMAP/ SuSiE, coloc, PRSice/LDpred/PRS-CS etc.

· Hands-on experience analyzing large-scale genomic datasets (e.g., WES/WGS, UK Biobank, All of Us, etc.)., and proteomic datasets (e.g., Olink, SomaScan) and associated analysis frameworks.

· Proficiency in cloud computing environments (e.g. AWS, DNAnexus) and HPC clusters.

· Experience with workflow automation (e.g.WDL, Nextflow).

· Proficiency with generating and maintaining reproducible pipelines (Git/GitHub) and experience with machine learning, deep learning large language models (LLMs) and AI applications.

Employer Description

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 everyon…