Senior Scientist, Discovery Research

Transcripta Bio

Palo Alto (CA)

Vor Ort

USD 90.000 - 130.000

Vollzeit

14 Tage+

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Zusammenfassung

A preclinical-stage AI drug discovery company in Palo Alto is seeking a Senior Scientist to lead wet lab operations including high-throughput drug screening and cell line maintenance. The ideal candidate will have a PhD in a relevant field, extensive experience with iPSC technology, and a collaborative spirit to work across teams. This is a hands-on role where your scientific expertise will guide project decisions and enhance our discovery engine. Competitive compensation and exciting startup environment await.

Qualifikationen

  • PhD in Cell Biology, Biochemistry, or related field with 3–5+ years of experience.
  • Experience in iPSC maintenance and differentiation.
  • Hands‑on experience with high-throughput drug screening workflows.

Aufgaben

  • Maintain and bank human cell lines.
  • Lead high-throughput drug screening campaigns.
  • Design and conduct downstream validation experiments.

Kenntnisse

iPSC maintenance
High-throughput drug screening
Experimental design
Data analysis using Python or R
Collaboration in cross-functional teams

Ausbildung

PhD in Cell Biology, Biochemistry, or related field
MS with 6+ years of relevant industry experience

Tools

Liquid handling automation (Hamilton, Tecan)
High-content imaging systems (Opera Phenix, ImageXpress)
ELISA and MSD

Jobbeschreibung

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.
WHAT YOU’LL BRING
  • 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.
  • Demonstrate excellent organizational skills and maintain high standards of documentation. Comfortable working in an ELN-based environment.
  • Collaborative and communicative: you thrive in cross-functional teams and can 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.
  • 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.
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.
  • You are comfortable with basic data analysis using Python, R, or similar tools.
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