Scientist – Computational Biology

An affiliate of Colossal is seeking a talented computational biologist with strong analytical skills to tackle challenging genotype-to-phenotype questions. The successful candidate will collaborate with scientists and engineers to design and perform bioinformatics analyses and integrate genomic, epigenomic, transcriptomic, and proteomic datasets. The candidate must have experience in bioinformatics, computational biology, or comparative genomics.

Preference will be given to candidates with a PhD who have experience leveraging and interpreting results of genotype-to-phenotype approaches that integrate across data sets, particularly applying diverse data (genomics, epigenomics, transcriptomics, proteomics) alongside functional annotations and ontologies to data-sparse or non-model organisms.

**This position will be based on-site full time in the Dallas, TX office.

Duties and Responsibilities:

• Run data analysis with biological data
• Run comparative, functional, and statistical genomics analysis
• Develop new tools in R/Python for data analysis and visualization
• Prepare reports and presentations to communicate findings to wet-bench biologists

Required Skills and Abilities:

• Two years of bioinformatics experience in the following areas: Comparative Genomics, Functional Genomics / Multi-Omics, Molecular Evolution, or Applied AI in Genomics.
• Familiarity in producing and curating genomic resources (e.g., reference genomes and gene annotations) for vertebrate organisms, especially in non-traditional model systems.
• Experience in the development of models to predict biological results or gene function, including variant detection, functional predictions, and genotype/phenotype relationships.
• Capable of leveraging knowledge across multiple levels of biological organization to validate the outputs of complex, integrative analyses.
• Demonstrated 2 years of experience with scripting languages, including but not limited to: Python, R, Perl, Ruby, Java, and BASH.
• Ability to write and run custom bioinformatics scripts using existing published tools and occasionally tools developed to summarize the results in a digestible manner and deliver the information using established reporting procedures.
• Proficiency with handling large-scale genomic data in an HPC (SGE, SLURM, PBS) Linux and/or cloud environment (e.g. AWS, Google Cloud, Azure).
• Experience in using GIT version control software and maintaining well-documented, reproducible notebooks and workflows.
• Ability to design and maintain databases (MySQL, PostgreSQL, MongoDB) and connect with visual platforms to curate and share data with non-bioinformatics team members.

Preferred Skills and Abilities:

• Calling germline and somatic sequence variants from high-coverage WGS, low-coverage WGS with imputation, and sequencing libraries from degraded or ancient DNA
• Executing rigorous analyses of diverse functional epigenomics approaches (e.g., RNA-seq and ATAC-seq) and integrating multi-omics datasets to aid in understanding of gene expression regulation
• Performing evolutionary and statistical genomics analyses, including population genetics analysis (e.g., runs of homozygosity and association mapping), genome-wide scans for evolutionary signatures and selective sweeps, and comparative genomics analyses associating genotype and phenotype (e.g., PAML inference of molecular evolution and phylogenetic regression)
• Constructing, interpreting, and utilizing pangenome graphs, whole genome alignments, and gene homology relationships
• Statistical planning and collaboration with laboratory scientists on designing well-powered experiments to generate useful multi-omics data sets
• Developing or implementing AI/ML frameworks and systems biology networks (e.g., interpretable aka visible deep neural networks like GenNet) for genotype-to-phenotype and functional predictions
• Understanding of precision gene editing technologies like CRISPR/Cas9 systems

Education and Experience:

• Masters with 2 years of experience or PhD in quantitative or basic science (computer science, computational biology, bioinformatics, chemistry, physics, or mathematics preferred) is required