Overview
Our client is a cutting-edge biotechnology organization working at the intersection of aging science, AI-driven biology, and drug discovery. Their mission is to develop therapies for age-related and chronic conditions by leveraging foundational models trained on large-scale longitudinal human data. Using advanced techniques to extract biological latent features and integrate genetics with multi-omics, the team is accelerating target discovery and therapeutic development beyond traditional clinical coding frameworks.
The company is seeking a Statistical Geneticist, Computational Biologist, or Bioinformatics Scientist experienced in deep learning applications for variant annotation and functional genomics. The hire will contribute to a next-generation target discovery platform, working with AI models, multi-omics resources, and population-scale genetic datasets to identify therapeutic targets and accelerate drug discovery. Responsibilities include processing genetic datasets (GWAS/PheWAS), building robust pipelines, implementing state-of-the-art methods, and delivering production-quality code. Ideal candidates have strong computational genomics expertise and an interest in translating biological data into real therapeutic insights.
Responsibilities
- Manage and preprocess genetics and tabular datasets, including plink-formatted data, GWAS summary statistics, and molQTL datasets.
- Conduct common and rare variant association studies (WGS/WES) 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.
Qualifications
Education: MSc or PhD in Statistical Genetics, Bioinformatics, Biostatistics, Computer Science, or a related quantitative discipline.
Experience Requirements:
- Demonstrated experience analysing large datasets using statistical inference and machine learning.
- Relevant scientific publications in reputable venues.
- Advanced Python and R programming skills.
- Comfort working in Unix/Linux environments.
- Practical experience with population genetics, GWAS, and post-GWAS analysis frameworks.
- HPC or cloud computing experience required.
- Training deep neural networks is considered a strong plus.
The ideal candidate is proactive, resourceful, and thrives in fast-moving, highly autonomous settings. Adaptability and curiosity about emerging scientific technologies are essential.
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.