Sr. Scientist I/II – Computational Biology

Responsibilities and Job Duties

• Multi-Omic Integration: 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).
• High-Performance Computing: Build, scale, and optimize processing pipelines within Cloud Computing to efficiently manage high-dimensional biomarker measurements.
• Provenance and reproducibility: Enforce strict version control, containerization, and rigorous documentation to ensure all analyses are fully reproducible, auditable, and transferable across the Translational Medicine team.
• Algorithmic Insight & Custom Analysis: Evaluate standard methodologies, identify limitations, and develop, maintain, or modify custom scripts and computational tools to address complex biological questions.
• Data Processing & Reporting: 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.
• Cross-Functional Collaboration: Partner closely with Clinical Development, Biostatistics, and Translational Medicine colleagues to co-create data analysis strategies, validate clinical hypotheses, and design robust translational frameworks.

Qualifications

Sr. Scientist I: 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.

Sr. Scientist II: PhD in a quantitative field (Computational Biology, Bioinformatics, Genetics, Genomics, Biophysics, Physics, or a related discipline). 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.

Knowledge, Skills, and Abilities

• 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.

Preferred Qualifications

• 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.