Data Scientist - Computational Biology

Contact: Neisha Camacho/Terra Parsons - 

teamnt@penfieldsearch.com

The ideal candidate has hands-on experience with RNA sequencing data, strong computational biology expertise, and enjoys collaborating closely with experimental scientists to generate biological insights that advance therapeutic discovery.

• Process, analyze, visualize, and interpret NGS datasets, including bulk RNA-seq, single-cell RNA-seq (scRNA-seq), and long-read RNA sequencing data.
• Perform bioinformatics analyses including quality control, sequence alignment, quantification, differential expression, isoform characterization, splicing analysis, and biological interpretation.
• Collaborate with cross-functional teams of experimental scientists, computational biologists, and research leaders to support target discovery and validation efforts.
• Translate computational findings into meaningful biological insights that inform research decisions.
• Evaluate and implement new bioinformatics tools, analytical methods, and emerging technologies relevant to transcriptomics and functional genomics.
• Develop scripts, workflows, and analytical pipelines to support reproducible and scalable data analysis.
• Contribute to study design, analytical strategy, and interpretation of research findings.
• Present results and communicate complex analyses clearly to technical and non-technical stakeholders.
• Deliver high-quality work while managing multiple priorities in a collaborative research environment.

• Education & Experience
• MS or PhD in Bioinformatics, Computational Biology, Systems Biology, Genomics, or a related scientific discipline.
• Relevant industry, academic, or postdoctoral research experience in computational biology or bioinformatics.

• Hands-on experience analyzing NGS datasets, particularly:
• Bulk RNA-seq
• Single-cell RNA-seq (scRNA-seq)
• Long-read RNA sequencing using Nanopore (required)
• Experience with PacBio sequencing is a plus.
• Strong understanding of transcriptomics, differential expression analysis, isoform discovery, and RNA splicing analysis.
• Proficiency in Python and/or R.
• Experience working in Linux environments and high-performance computing (HPC) or cloud platforms such as AWS.
• Familiarity with genome annotation resources, biological pathway databases, and systems biology concepts.
• Commitment to reproducible research, documentation, and version control best practices.

• Experience with additional functional genomics data such as ATAC-seq, ChIP-seq, or PRO-seq.
• Drug discovery or biotechnology industry experience.
• Experience collaborating with laboratory scientists in a multidisciplinary research environment.

What We're Looking For

We're seeking someone who is curious, collaborative, and scientifically driven, with excellent communication skills and the ability to work closely with both computational and experimental teams. This individual should be comfortable presenting previous research, explaining analytical approaches, and contributing to a fast-paced drug discovery environment.