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. #J-18808-Ljbffr
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