Computational Biologist

Transcripta Bio Job Posting

Transcripta 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 Do

• Develop, 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 Bring

• PhD 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 Have

• Experience 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)