Senior Scientist, Discovery Research

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 Scientist to become a cornerstone of our wet lab operations. You will own key areas of our experimental platform – from cell culture and high‑throughput drug screening to the downstream assays that validate hits and guide program decisions. This is a hands‑on role with real scientific ownership, where your work directly shapes the data that powers our discovery engine.

WHAT YOU’LL DO

Maintain, expand, and bank disease‑relevant human cell lines, including induced pluripotent stem cells (iPSCs) and iPSC‑derived types, while ensuring consistent quality and reproducibility. Lead high‑throughput drug screening campaigns utilizing automated liquid handlers and plate‑based platforms. Design screening workflows, ensure data integrity, and resolve technical issues efficiently. Design and conduct downstream validation experiments to confirm screening hits and characterize mechanisms of action, utilizing high‑content imaging, qPCR, immunocytochemistry, Western blot, ELISA, and quantitative protein assays. Develop and optimize cell‑based assays for disease‑relevant biological readouts, collaborating with computational and therapeutic teams to align experimental outputs with platform requirements. Translate complex datasets into clear scientific narratives. Present findings at internal meetings and contribute to reports, publications, and external communications. Maintain detailed records in the electronic laboratory notebook (ELN) and contribute to standard operating procedures (SOPs), protocol documentation, and best practice development as the organization expands. Serve as a technical resource for junior team members by providing daily guidance and supporting a culture of scientific excellence. Support lab operations, including reagent preparation, equipment maintenance, and vendor coordination.

QUALIFICATIONS

PhD in Cell Biology, Biochemistry, Molecular Biology, Pharmacology, or a closely related field with 3–5+ years of industry or postdoctoral experience; or MS with 6+ years of relevant industry experience. Demonstrated expertise in iPSC maintenance, differentiation, and quality assessment. Experience with primary human cells or disease‑relevant iPSC‑derived cell types is strongly preferred. Hands‑on experience running or supporting high‑throughput drug screening workflows, including familiarity with liquid handling automation (e.g., Hamilton, Tecan, Beckman, or equivalent). Proficiency in downstream validation techniques, including high‑content imaging and analysis (e.g., Opera Phenix, ImageXpress), immunocytochemistry, Western blot, and quantitative protein assays (ELISA, MSD, or equivalent). Strong experimental design instincts: ability to independently scope assays, troubleshoot, and interpret data with scientific rigor and speed. Excellent organizational skills and high standards of documentation. Comfortable working in an ELN‑based environment. Collaborative and communicative: thrive in cross‑functional teams and translate bench‑level findings for computational colleagues and leadership alike. Thrives in a fast‑paced, resource‑constrained startup environment where adaptability and initiative are essential.

NICE TO HAVE

Background in rare genetic disease, neurodegenerative disease, or another genetically defined disease area. Experience with functional genomics approaches, including CRISPR‑based screens or pooled genetic perturbation assays. Familiarity with transcriptomics methods (bulk or single‑cell RNA‑seq) or experience working with bioinformatics teams to interpret experimental data is a plus. Comfortable with basic data analysis using Python, R, or similar tools. #J-18808-Ljbffr Next Matter