Translational Medical Scientist
NateraFull Time
Mid-level (3 to 4 years), Senior (5 to 8 years)
Candidates must possess a Master's degree in bioinformatics, data science, computer science, or a related field, with 2 to 4+ years of hands-on experience in processing -omics data and developing standardized, reproducible processing pipelines. A strong understanding of core biological concepts such as human genetics, RNA biology, and genomics is essential, along with basic knowledge of common bioinformatics tools (e.g., SAMTools, BCFTools, DESeq2, BWA/STAR) and file formats (BAM, VCF). Proficiency in Python programming and command-line (shell) skills is required, as is experience with high-throughput or cloud-based computing. Excellent documentation, communication, and interpersonal skills are also necessary.
The (Senior) Bioinformatician will develop workflows for -omics data processing and analysis, including whole genome, whole exome, array, RNA-seq, proteomic, and single-cell -omics data. Responsibilities include interpreting complex -omics analyses to generate biological hypotheses and insights, working with ML scientists to develop model evaluation workflows, and collaborating with engineering teams to create robust tools and software packages. The role also involves generating data interfaces like visualizations and dashboards, implementing novel computational tools, and contributing to the construction and application of foundation models using large-scale genomics and transcriptomic datasets. The bioinformatician will also help process, analyze, and draw conclusions from multi-modal functional genomics data for target discovery and build, execute, and maintain automated pipelines for large-scale -omics data analysis.
AI-driven drug discovery and development
Deep Genomics focuses on drug development in the biotechnology sector by utilizing artificial intelligence to explore RNA biology and discover potential therapies for genetic conditions. The company's main product, the AI Workbench, employs data-driven predictions to identify new drug targets. This tool has evolved over time, with the latest version, AI Workbench 3.0, set to enhance its capabilities in targeting complex genetic diseases. Deep Genomics serves a diverse clientele, including pharmaceutical companies and research institutions, and generates revenue through the development and licensing of its AI Workbench. The goal of Deep Genomics is to accelerate the drug discovery process and improve treatment options for patients suffering from genetic disorders.