Senior Scientist

Salary Range: 150000 to 200000 (Currency: USD) (Pay period: per-year-salary)

Position Summary:

We are seeking an experienced and versatile Senior Scientist, Computational Biology to join our Data Science team and contribute across the breadth of Korro’s research and translational programs. The successful candidate will operate as an analytical generalist — comfortable moving between target validation, NGS/omics analysis, assay analytics, and translational support for our oligonucleotide therapeutics pipeline. This is a hands-on role suited to someone who has already proven themselves in a biotech or pharma environment and who thrives in a small, fast-moving organization where program needs evolve quickly.

Key Responsibilities:

• 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.

• Design, execute, and interpret NGS-based experiments (RNA-seq, variant calling, targeted amplicon sequencing) in support of oligonucleotide design, screening, and pharmacology workstreams.

• 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.

• Develop and maintain reproducible analysis pipelines in R and/or Python, leveraging existing Data Science infrastructure (Posit Connect, MLflow, containerized environments, AWS).

• 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.

• Collaborate with cross-functional stakeholders to scope new analyses, set realistic timelines, and deliver results that are technically sound and decision-ready.

• Mentor junior scientists and contractors on best practices in scientific computing, reproducibility, and statistical rigor.

• Share findings through internal presentations and contribute to the scientific community through publications or presentations as appropriate.

Required Qualifications:

• 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.

• 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.

• Strong scientific programming in R and/or Python, with proficiency in standard biological data analysis libraries (Bioconductor, tidyverse, pandas, numpy, scikit-learn, etc.).

• Solid experience analyzing NGS data, including RNA-seq differential expression workflows, variant calling, and quality control of sequencing experiments.

• 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.

• Working knowledge of statistical modeling appropriate to biological data: linear and generalized linear models, mixed-effects models, multiple testing correction, and basic experimental design.

• Comfort working with messy, real-world experimental data and collaborating directly with bench scientists to refine analytical questions.

• Excellent written and oral communication skills, including the ability to translate technical results for non-computational stakeholders.

Preferred Qualifications:

• Experience analyzing clinical or translational data — Phase 1 PK/PD, biomarker time-courses, or exposure-response analyses — is a strong plus.

• Familiarity with hepatology, metabolic disease, or other indications relevant to GalNAc-conjugated oligonucleotide therapeutics.

• Experience with cloud-based analytical infrastructure (AWS), containerized workflows (Docker), and reproducible reporting tools (Posit Connect, Quarto, R Markdown).

• Prior contribution to IND-enabling packages or regulatory submissions.

• Peer-reviewed publications in computational biology, genomics, or therapeutic pharmacology.

• 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.