Scientist

• Support routine computational analysis of functional genomics and transcriptomics datasets using established internal pipelines and reporting systems.
• Assist with experimental registration, source sample tracking, and data management for NGS-based workflows to ensure data retention, visibility, and accessibility.
• Manage workflow and data entry in Benchling OEMS for pooled gene perturbation, amplicon-seq, bulk RNA-seq, and related NGS experiments.
• Process pooled genomic perturbation screen data, including raw FASTQ files, using established NextFlow pipelines and internal gene-edited reporting systems.
• Support future arrayed perturbation screening efforts by running established analytical pipelines once developed and implemented.
• Process Perturb-seq datasets by converting raw FASTQ files into count tables using established Cell Ranger workflows.
• Support bulk RNA-seq routine by downloading data from CROs, processing samples through the internal bulk RNA-seq pipeline and generating reports using established RNA-seq reporting workflows.
• Assist with transcriptomic assessment of edited clones using existing analysis pipelines.
• Partner closely with the manager and TD&B team members to prioritize analyses, troubleshoot routine issues, and ensure timely project completion.
• Maintain organized records of analyses, inputs, outputs, and reports to support reproducibility, data integrity, and downstream interpretation.

• Degree or experience in bioinformatics, computational biology, genomics, genetics, biology, data science, or related discipline.
• Familiarity with NGS data types, including pooled gene perturbation screens, bulk RNA-seq, amplicon-seq, Perturb-seq, or other functional genomics workflows.
• Experience running established computational pipelines and working with structured data outputs.
• Familiarity with sequencing file formats and analysis outputs, including FASTQ files, count matrices, and standard NGS reports.
• Experience with Benchling, sample tracking systems, or other experimental registration/data management platforms.
• Experience working in Unix/Linux environments and cloud-based computing platforms, including AWS, is preferred.
• Basic programming or scripting experience in R, Python, or shell scripting is preferred.
• Strong attention to detail and commitment to data integrity, reproducibility, and documentation.
• Ability to work independently on routine analyses while communicating regularly with the manager and project teams.
• Strong written and verbal communication skills, with the ability to collaborate effectively in a cross-functional research environment.

• Ability to run established computational workflows and pipelines
• Diligence and organization for metadata entry
• Ability to execute on a list of tasks without close supervision

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