Position Summary - We are seeking an experienced Bioinformatics Analyst to support the analysis, visualization, and registration of mass spectrometry (MS)-based proteomics data generated from tissue and plasma samples. This role requires a unique blend of computational biology expertise, data engineering, and interactive web development. The ideal candidate will work closely with cross-functional teams, including wet-lab scientists, to process, curate, and integrate proteomic datasets into internal systems for early-stage drug discovery and target feasibility assessments. Key Responsibilities - • Apply domain expertise to process, filter, and analyze high-dimensional MS-based proteomics data. • Develop, document, and maintain data analysis workflows for scientific reproducibility and manuscript publication. • Transform and curate datasets to meet metadata standards and enable downstream computational analysis. • Register datasets into internal data registries with version control and metadata capture. • Design and develop interactive, user-friendly web-based visualization tools (e.g., dashboards) accessible by both technical and non-technical stakeholders. • Collaborate with wet-lab scientists and project teams to translate scientific questions into data analysis and visualization solutions. Required Qualifications - • PhD in Bioinformatics, Computational Biology, Biochemistry, Molecular Biology, or related field • MS + 2 years, or BS + 5 years relevant experience. • Demonstrated experience working with mass spectrometry-based proteomics datasets. • Proficiency in scripting languages such as R and/or Python. • Experience developing graphical user interfaces (GUI) and/or web applications for scientific data exploration. • Familiarity with data transformation, curation, and registration into relational or graph databases. Preferred Qualifications - • Wet-lab experience in molecular biology, proteomics, or analytical chemistry. • Familiarity with shell scripting and cloud computing infrastructure (e.g., AWS, GCP). • Knowledge of version control (Git), workflow documentation, and reproducible research practices.