Senior Bioinformatics Scientist | Transcripta Bio · Teeming.ai
Transcripta Bio
Transcripta Bio is a pioneering drug discovery company leveraging a proprietary AI-driven platform that integrates high-throughput transcriptomic data and machine learning to map chemical structures…
Transcripta Bio is a pioneering drug discovery company leveraging a proprietary AI-driven platform that integrates high-throughput transcriptomic data and machine learning to map chemical structures…
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.
We are looking for a Senior Bioinformatician who is technically exceptional and biologically curious: someone who can develop and run robust analysis pipelines, extract signal from complex high-throughput datasets, and translate results into clear scientific findings that move programs forward. You will be an integral contributor to the computational biology team, working at the interface of experimental biology, data science, and drug discovery.
Role Description
Develop, maintain, and optimize reproducible bioinformatics pipelines for the 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.
Qualifications
PhD in Bioinformatics, Computational Biology, Genomics, or a related field with 3+ years of relevant experience; or MS/BS with 5+ years of strong industry experience in bioinformatics.
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.
Preferred Qualifications
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).
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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 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.
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.
Strong written and verbal communication skills; able to present results and methods clearly to scientists across disciplines.
Self-starter mentality: comfortable operating with autonomy in a fast-moving startup environment while knowing when to ask for alignment.
