Lead Bioinformatics Analyst

• Lead analysis of high-throughput multi-omic data to investigate tumor evolution, therapeutic resistance, and radiation-related phenotypes
• Integrate clinico-genomic and spatial-omics modalities using machine learning and custom statistical methods
• Direct bioinformatics operations and advise on complex oncological datasets
• Develop educational materials for highly technical cancer biology research
• Lead genomic, transcriptomic, and proteomic analyses to identify biomarkers and patient-specific insights
• Develop and integrate multi-modal omic datasets for computational modeling and translational research
• Collaborate with faculty, clinicians, bioinformaticians, and research partners nationally and internationally
• Provide scientific and programmatic leadership, including establishing program structure, priorities, capabilities, technologies, and processes
• Define and oversee program goals, resources, milestones, deliverables, and services
• Conduct complex analyses, develop recommendations, and present program outcomes and strategic priorities
• Lead development and implementation of data-driven research initiatives and computational strategies
• Recommend and implement analytical methodologies, infrastructure, software platforms, and technology investments
• Establish program standards, governance, workflows, change-management policies, and technical documentation
• Lead strategic collaborations across multidisciplinary teams and serve as a scientific and programmatic liaison
• Manage personnel, research and office space, computational infrastructure, software, equipment, supplies, and technical resources
• Participate in recruitment, hiring, onboarding, training, supervision, mentorship, performance management, and professional development of staff
• Lead and oversee internship and trainee programs
• Perform other duties as assigned

• PhD and 3 years relevant experience required
• Or equivalent combination of education and experience
• Strong knowledge of data mining algorithms, including machine learning techniques required
• Scientific insight and statistics background in the field of cancer research, biology, and bioinformatics required
• Familiarity with next generation sequencing analysis workflows required
• Strong proficiency with one or more programming languages, such as R and Python required
• Excellent communication, interpersonal, verbal, presentation and written skills required

Demonstrates expertise in leading multi-omic data analysis, integrating clinico-genomic and spatial-omics modalities, and applying machine learning techniques in cancer research. Proven ability to provide scientific leadership, manage multidisciplinary teams, and develop educational materials in a complex research environment.

• PhD In Relevant Field
• Data Mining Algorithms
• Machine Learning Techniques
• Next Generation Sequencing Analysis
• Programming Proficiency In R And Python

• Multi-Omic Data Analysis
• Genomic Analysis
• Transcriptomic Analysis
• Proteomic Analysis
• Statistical Methods
• Computational Modeling
• Analytical Methodologies
• Data-Driven Research Initiatives
• Technical Documentation
• Program Management

• Excellent Communication Skills
• Interpersonal Skills
• Presentation Skills
• Mentorship
• Team Leadership

• Cancer Research
• Bioinformatics
• Tumor Evolution
• Therapeutic Resistance
• Radiation-Related Phenotypes
• Clinico-Genomic Integration
• Spatial-Omics
• Biomarkers
• Translational Research
• Program Governance

• Bioinformatics Software
• Statistical Software
• Computational Infrastructure
• Data Analysis Platforms
• Change-Management Tools