Associate Principal Scientist, Systems Biology

ASSOCIATE PRINCIPAL SCIENTIST – SYSTEMS BIOLOGY

The Vaccines & Advanced Biotechnologies department, part of our Research & Development Division, is seeking a passionate and talented systems biologist with expertise in generating and analyzing complex multi‑dimensional biological data. This role, based at our West Point, PA location, will develop and implement experimental and computational tools in a scientifically rigorous environment, collaborating across experimental and computational biology, data science, and other disciplines to advance a diverse therapeutic pipeline.

• Directly contribute to multiple stages of therapeutic development through the design and implementation of high‑dimensional functional genomics and cellular profiling assays, including in‑vitro and in‑vivo pooled/arrayed CRISPR screens, single‑cell transcriptomics, lineage tracing, and experimental and in‑silico derived proteomic datasets.
• Perform statistically rigorous quantitative analyses while practicing reproducible research and data‑integrity standards on large‑scale biological datasets.
• Analyze, interpret, and summarize complex biological data, employing data‑visualization tools to communicate findings to broader audiences.
• Identify, analyze, and integrate internal and external data and knowledge resources to enable data mining in a biological context and inform actionable learnings for pipeline development.
• Develop and implement experimental and/or computational methods to integrate findings from functional genomics screens with other datasets (RNA‑Seq, DRUG‑seq, WGS, CRISPR, single‑cell RNA‑Seq, proteomics, metabolomics) and additional biological data streams to generate deep biological knowledge and drive innovation.
• Represent systems biology expertise in cross‑functional teams by interpreting high‑dimensional data and proposing candidates for further computational or biological interrogation.
• Engage with cross‑functional teams to translate biological hypotheses into experimental and/or computational interrogations of functional genomics data and multi‑omics molecular profiles to drive iterative experimental design decisions.
• Work in a highly collaborative environment, tightly embedded in project teams across genetics, chemistry, pharmacology, and molecular and cellular biology, to accelerate therapeutic development.
• Survey relevant literature on novel experimental and computational methods, introduce them into our in‑house workflows, keep up to date on project‑relevant literature, attend conferences, and participate in workshops to advance professional growth.

Ph.D. in Systems Biology, Computational Biology, Computer Science, Biostatistics, Genetics, Immunology, Mathematics, Molecular Biology, Statistics, or a related field, and a minimum of 4years of experience. Experience may be a mix of academic and industrial backgrounds.

• Applied experience:
  Demonstrated experience with experimental and/or computational strategies and interpretation of large‑scale biological data, such as sequencing modalities and pooled/arrayed CRISPR‑based screens.
• Fluency in generating and analyzing diverse large‑scale NGS datasets (RNA‑seq, WES, scRNA‑seq, spatial transcriptomics, etc.).
• Experience with statistical hypothesis‑testing methodology, machine‑learning concepts and methods, and integrating results from multiple data sources to extract meaningful biological insights.
• Technical skills:
  Fluency with experimental and/or computational methods for high‑dimensional data generation and interrogation; programming skills in R (including Rshiny and tidyverse), Python, MATLAB; ability to create interpretable visualizations.
• Laboratory experience with cell culture, RNA/DNA extractions, library preparation for sequencing, molecular biology tools, and experimental design strategies.
• Experience with HPC, AWS cloud infrastructure, and Linux environments; familiarity with version control systems such as Git.
• Ability to adapt to and work with existing analytic frameworks.

• Understanding the pros and cons of algorithms for DNA‑seq, RNA‑seq, single‑cell…