Sr. Scientist I/II – Computational Biology

MacroGenics, Inc.•Rockville, MD

1d•$115,600 - $201,300

About The Position

We are seeking a highly motivated and intellectually curious Computational Biologist to join our Translational Bioinformatics group. Operating in a highly collaborative and dynamic environment, this role is designed for a hands-on builder who thrives on high-quality biological data science that bridges biology and the clinic. You will serve as a vital technical partner, translating raw data (genomics, proteomics, flow cytometry, pathology) into actionable biological and clinical insights that drive our pipeline forward. The ideal candidate possesses a deep understanding of quantitative data science, a strong foundation in oncology or immunology, and a proven track record of writing clean, scalable code to answer complex therapeutic questions.

Requirements

PhD in a quantitative field (Computational Biology, Bioinformatics, Genetics, Genomics, Biophysics, Physics, or a related discipline).
0 – 2 years of full-time, post-PhD experience (academic or industry) demonstrating independent scientific project ownership, cross-functional teamwork, and navigating a fast-paced environment.
3+ years of full-time, post-PhD experience (academic or industry) demonstrating independent scientific project ownership, cross-functional teamwork, and navigating a fast-paced environment.
Experience across standard modern biological data types including gene expression, genome sequencing, mutational profiling, and proteomics.
Demonstrated comfort and autonomy working within Cloud Computing environments (AWS, Google Cloud) and/or traditional Linux High-Performance Computing (HPC) cluster infrastructures.
Advanced proficiency in Python for data wrangling, statistical analysis, and data visualization. Experience with best software development practices, including rigorous documentation, testing, and version control (git).
Ability to write robust, production-quality code completely independent of AI or agentic coding assistants. Deep understanding of the algorithmic basis behind common sequencing and data analysis tools.
Demonstrated evidence of building published software.

Nice To Haves

Prior hands-on experience working within Oncology or Immunology pathways, target identification, or biomarker discovery.
Prior hands-on experience with AI/ML-enabled image analysis workflows.
2+ years working within a Biotech, Pharma, or structured translational research environment with cross-functional interactions.
Familiarity with or exposure to agentic coding platforms.
Experience handling data hand-offs and quality control from external CROs and proteomics/genomics vendors.

Responsibilities

Lead the technical execution and integration of diverse datasets across multiple pipelines. Manage data ingestion from external CROs and internal labs, combining internal high-dimensional datasets (Flow Cytometry, Digital Pathology) with large-scale molecular profiling data (Genomics, Transcriptomics, Proteomics).
Build, scale, and optimize processing pipelines within Cloud Computing to efficiently manage high-dimensional biomarker measurements.
Enforce strict version control, containerization, and rigorous documentation to ensure all analyses are fully reproducible, auditable, and transferable across the Translational Medicine team.
Evaluate standard methodologies, identify limitations, and develop, maintain, or modify custom scripts and computational tools to address complex biological questions.
Translate raw, complex multi-omic data into clear, high-fidelity scientific data packages and executive-ready visualizations that support informed decision-making related to clinical programs and asset development. Build, maintain, and document robust, reproducible code infrastructure to streamline data extraction, enabling rapid-turnaround on cross-functional reporting.
Partner closely with Clinical Development, Biostatistics, and Translational Medicine colleagues to co-create data analysis strategies, validate clinical hypotheses, and design robust translational frameworks.