Computational Biologist

Transcripta Bio Job PostingTranscripta Bio is a preclinical-stage AI drug discovery company pioneering a patient-first approach to therapeutics. Headquartered in Palo Alto, CA, we have built a proprietary closed-loop discovery engine - comprising our Disease Signature Atlas, Drug-Gene Atlas, and Conductor AI platform - that integrates single-cell patient transcriptomics, causal human genetics, and pre-validated chemistry to identify and advance drug candidates with a structural edge over conventional approaches.What You'll DoDevelop, maintain, and optimize reproducible bioinformatics pipelines for processing, QC, and analysis of high-throughput datasets, including bulk RNA-seq, single-cell RNA-seq, and high-content imaging data.Analyze data from drug perturbation screens to identify transcriptomic signatures, compound-gene associations, and patterns of drug response across disease-relevant cell models.Integrate data across multiple experimental modalities (transcriptomics, imaging, protein measurements) to build a coherent picture of biology and prioritize therapeutic hypotheses.Partner with wet lab scientists to help design experiments, define data standards, troubleshoot data quality issues, and ensure clean handoffs between experimental and computational workflows.Contribute to the curation and expansion of the Drug-Gene Atlas: ensure that data inputs are well characterized, analysis methods are calibrated, and outputs are interpretable and reliable.Communicate findings clearly through reports, visualizations, and presentations to both computational and non-computational colleagues.Stay current with advances in transcriptomics, single-cell methods, and computational biology; evaluate and adopt new tools and approaches where they add value.Contribute to code review, documentation, and best practices as the team grows.What You'll BringPhD in Bioinformatics, Computational Biology, Genomics, or a related field with 3+ years of relevant experience in industry.Extensive hands-on experience processing and analyzing bulk and/or single-cell RNA-seq data, from raw reads through QC, normalization, dimensionality reduction, clustering, and differential expression.Experience in relevant scientific packages (e.g., scanpy, pandas, numpy, DESeq2, ggplot2) and comfort working in a Linux/command-line environment. Strong programming proficiency in Python and/or R is a plus.Experience building and running reproducible workflows using tools such as Snakemake, Nextflow, or equivalent; familiarity with version control (Git) and best practices for collaborative code development.Exposure to high-throughput or perturbational screening datasets (chemical, genetic, or combined) is highly desirable.A biologically grounded mindset: you approach data with mechanistic questions in mind, not just statistical outputs.Nice To HaveExperience analyzing data from functional genomics assays (e.g., ATAC-seq, ChIP-seq, perturb-seq, or pooled CRISPR screens).Familiarity with spatial transcriptomics or multimodal data integration approaches.Experience working with or alongside ML/AI teams; familiarity with applying machine learning methods to biological data.Background in rare genetic disease, neurodegeneration, or other genetically defined disease areas.Experience in cloud-based compute environments (AWS, GCP, or equivalent)