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 candidate will be working under the supervision of Dr. Nathalie Chami. 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.

• 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.

 

• 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.