Statistical Geneticist (AI/Genomics)

TL;DR

Statistical Geneticist (AI/Genomics): Developing a next-generation target discovery platform leveraging AI models, multi-omics, and population-scale genetic datasets with an accent on processing genetic datasets and building robust pipelines. Focus on applying classical machine learning and deep learning approaches to functional genomics problems and accelerating drug discovery.

Location: Fully remote

Company

Our client is a cutting-edge biotechnology organization focused on developing therapies for age-related and chronic conditions using AI-driven biology and drug discovery.

What you will do

• Manage and preprocess diverse genetic and tabular datasets, including plink-formatted data, GWAS summary statistics, and molQTL datasets.
• Conduct common and rare variant association studies and post-GWAS analyses, including colocalization, Mendelian randomization, and integration with transcriptomic/proteomic data.
• Apply classical machine learning and deep learning approaches to functional genomics problems.
• Develop, maintain, and scale automated pipelines for association studies while ensuring code quality and reproducibility.

Requirements

• MSc or PhD in Statistical Genetics, Bioinformatics, Biostatistics, Computer Science, or a related quantitative discipline.
• 3+ years of relevant hands-on experience analyzing large datasets using statistical inference and machine learning.
• Advanced Python and R programming skills.
• Experience working in Unix/Linux environments.
• Practical experience with population genetics, GWAS, and post-GWAS analysis frameworks.
• HPC or cloud computing experience is required.

Nice to have

• Training deep neural networks.

Culture & Benefits

• Competitive compensation aligned with industry expectations.
• Fully remote work environment with flexible scheduling.
• High-trust, low-bureaucracy culture emphasizing ownership and accountability.
• Direct impact on core therapeutic discovery efforts from day one.
• Support for scientific publication and research visibility.