Bioinformatics Scientist - Gene Regulation & Cellular Reprogramming at e184 Repro

Setup: On-site
Location: Portland, Oregon
Type: Full-time
Level: senior
Posted: Apr 27, 2026

Description

e184 Repro is hiring a Bioinformatics Scientist to lead computational analysis of multi-modal genomics data for cellular reprogramming. The role focuses on single-cell RNA-seq and ATAC-seq analysis, gene regulatory network inference, differential accessibility, trajectory mapping, TF cocktail prioritization, and pooled perturbation or CRISPRa screen analysis. The scientist will collaborate with wet-lab teams to translate computational predictions into experimental designs and may explore foundation-model approaches. The position requires a PhD or an MS with substantial relevant experience, strong Python and R skills, and a strong publication record.

Required Skills

scRNA-seq
ATAC-seq
GRN inference
Python
R
Scanpy
Seurat
Trajectory inference
CRISPRa

Additional Information

Minimum Education: PhD in Bioinformatics, Computational Biology, or a related quantitative field; alternatively, an MS with at least 5 years of relevant industry experience.
Required Previous Experiences: Experience applying computational TF ranking and GRN inference to cellular reprogramming, transdifferentiation, directed differentiation, or iPSC systems.
Hands-on multi-platform single-cell RNA-seq analysis, including troubleshooting and quality control.
Multi-modal genomics analysis, including ChIP-seq, CUT&RUN, or ATAC-seq.
Experience with established gene regulatory network inference methods.
Experience analyzing pooled perturbation screens with single-cell or bulk readouts.
Experience with trajectory inference and pseudotime methods.
Programming and statistical analysis experience with Python, R, Scanpy, and Seurat.
Experience with cloud platforms, workflow managers, containerization, and version control.
Strong publication record and cross-functional collaboration with experimental biologists.
Preferred Qualifications: Experience nominating or validating TF cocktails that successfully induced cell state conversion.
Experience with dynamical systems modeling or inverse problem approaches for TF combination ranking.
Background in advanced trajectory inference, Bayesian genomics, multi-omics integration, or cross-species comparative regulatory genomics.
Familiarity with transformer architectures in genomics and interest in hybrid classical/ML approaches.
Translation Company Name: e184 Repro