Bioinformatics Scientist - Gene Regulation & Cellular Reprogramming

e184•Portland, OR

13h•Onsite

About The Position

e184 Repro is a biotechnology research company with the mission of advancing in vitro gametogenesis to solve one of biology’s most profound challenges: returning the fundamental right to procreate. We work at the frontier of cutting-edge technology, integrating cellular reprogramming, machine learning-guided optimization, multi-omics analysis, and automated experimental workflows to enable gamete development for individuals facing reproductive challenges. As a Bioinformatics Scientist with a cellular reprogramming background, you will lead computational analysis of multi-modal genomics data (scRNA-seq, ATAC-seq) to identify transcription factor combinations driving desired cell state conversion. This role focuses on gene regulatory network inference, differential analysis of single-cell transcriptomics, and computational prioritization of TF cocktails for cellular reprogramming, requiring deep expertise in multi-platform scRNA-seq analysis and transcriptional regulation biology. You will collaborate closely with wet lab teams to translate computational predictions into experimental designs, while also exploring hybrid approaches that integrate foundation model insights into our reprogramming pipeline.

Requirements

PhD in Bioinformatics, Computational Biology, or related quantitative field (or MS with 5+ years relevant industry experience)
Demonstrated track record applying computational TF ranking and GRN inference to cellular reprogramming problems, transdifferentiation, directed differentiation, or iPSC systems
Multi-platform single-cell RNA-seq expertise: hands-on analysis from at least two different platforms, including platform-specific troubleshooting and quality control
Multi-modal genomics proficiency: ChIP-seq, CUT&RUN, or ATAC-seq analysis including peak calling, differential accessibility, and TF motif enrichment
Hands-on experience with established GRN inference methods to nominate or rank regulators of cell state, beyond literature-curated lists
Experience analyzing pooled perturbation screens (CRISPRa, CRISPR knockout, or barcoded TF overexpression) with single-cell or bulk readouts
Working knowledge of trajectory inference and pseudotime methods for mapping cell state transitions
Strong programming skills in Python and R, with proficiency in Scanpy/Seurat and statistical analysis for high-dimensional data
Comfortable working in a modern computational environment: cloud platforms, workflow managers, containerization, and collaborative version control
Strong publication record and demonstrated cross-functional collaboration with experimental biologists

Nice To Haves

Direct experience nominating or validating TF cocktails that successfully induced a cell state conversion (published or in preparation).
Experience with dynamical systems modeling for cell state transitions, or inverse problem approaches for TF combination ranking.
Background in advanced trajectory inference (optimal transport, GRN dynamics over pseudotime), Bayesian genomics, multi-omics integration, or cross-species comparative regulatory genomics.
Familiarity with transformer architectures in genomics and interest in hybrid classical/ML approaches to gene regulation.

Responsibilities

Lead end-to-end TF discovery for cellular reprogramming - from multi-platform single-cell genomics analysis (scRNA-seq, ATAC-seq) through GRN inference, differential analysis, and trajectory mapping - to nominate the regulators that flip cell fate.
Crack the combinatorial code of reprogramming by ranking TF cocktails as actionable combinations and decoding pooled perturbation and CRISPRa screens at single-cell resolution.
Read regulatory grammar straight off the chromatin - accessibility, motifs, synergy, repression - and build the data backbone that harmonizes modalities and platforms into something we can actually model on.
Sit shoulder-to-shoulder with wet lab teammates, closing the loop between predictions and screens: ingest fresh NGS readouts, retrain, re-prioritize, and pick the next experiment that teaches the model the most.