Senior Scientist, Computational Biology at Korrobio
Korrobio · 60 First St, United States Of America · Hybrid
- Office in 60 First St
Description
Position Summary:
Key Responsibilities:
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Drive computational biology analyses across Korro’s research pipeline, including target validation, mechanism-of-action studies, and characterization of RNA editing activity in preclinical models.
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Design, execute, and interpret NGS-based experiments (RNA-seq, variant calling, targeted amplicon sequencing) in support of oligonucleotide design, screening, and pharmacology workstreams.
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Partner with platform biology, chemistry, and pharmacology teams to translate experimental questions into rigorous analytical workflows and to communicate results in a form that informs program decisions.
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Develop and maintain reproducible analysis pipelines in R and/or Python, leveraging existing Data Science infrastructure (Posit Connect, MLflow, containerized environments, AWS).
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Contribute analytical support to the preclinical-to-clinical translational handoff for our GalNAc-conjugated programs, including PK/PD characterization and biomarker analysis as programs mature.
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Collaborate with cross-functional stakeholders to scope new analyses, set realistic timelines, and deliver results that are technically sound and decision-ready.
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Mentor junior scientists and contractors on best practices in scientific computing, reproducibility, and statistical rigor.
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Share findings through internal presentations and contribute to the scientific community through publications or presentations as appropriate.
Required Qualifications:
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Ph.D. in Computational Biology, Bioinformatics, Genomics, Computational Chemistry, or a related quantitative discipline, with 3+ years of post-PhD experience in a biotech or pharmaceutical industry setting. Academic-only or pure-tech industry experience will not be considered for this role.
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Demonstrated track record of shipped, decision-impacting analyses on industry programs — e.g., target validation, biomarker discovery, oligonucleotide or small-molecule pharmacology, or preclinical-to-clinical translation.
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Strong scientific programming in R and/or Python, with proficiency in standard biological data analysis libraries (Bioconductor, tidyverse, pandas, numpy, scikit-learn, etc.).
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Solid experience analyzing NGS data, including RNA-seq differential expression workflows, variant calling, and quality control of sequencing experiments.
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Familiarity with oligonucleotide or RNA-based therapeutics — including chemical modifications, delivery modalities (GalNAc, LNP), and the analytical considerations specific to ASOs, siRNAs, or related modalities.
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Working knowledge of statistical modeling appropriate to biological data: linear and generalized linear models, mixed-effects models, multiple testing correction, and basic experimental design.
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Comfort working with messy, real-world experimental data and collaborating directly with bench scientists to refine analytical questions.
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Excellent written and oral communication skills, including the ability to translate technical results for non-computational stakeholders.
Preferred Qualifications:
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Experience analyzing clinical or translational data — Phase 1 PK/PD, biomarker time-courses, or exposure-response analyses — is a strong plus.
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Familiarity with hepatology, metabolic disease, or other indications relevant to GalNAc-conjugated oligonucleotide therapeutics.
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Experience with cloud-based analytical infrastructure (AWS), containerized workflows (Docker), and reproducible reporting tools (Posit Connect, Quarto, R Markdown).
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Prior contribution to IND-enabling packages or regulatory submissions.
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Peer-reviewed publications in computational biology, genomics, or therapeutic pharmacology.
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Hands-on experience using agentic coding assistants (e.g., Claude Code, Cursor, GitHub Copilot) in a production scientific computing context, with a thoughtful perspective on where these tools accelerate work and where human review remains essential.
