The Department of Medicine’s Division of Oncology at the Dell Medical School is seeking a Data Analyst I to carry out computational research in a highly collaborative and interdisciplinary environment with world-class experts and state-of-the-art technologies to focus on translational research models to define molecular alterations associated with the processes targeted by various cancer therapies.
Requirements
- Experience with statistical analyses and working in a high-performance computing environment.
- Experience with Docker, continuous improvement/continuous deployment management
- Previous experience using R, Python, and SQL for statistical and computational analyses.
- Management and control of versions using Docker and Git
- Development experience of software packages and/or interfaces
- Data integration across multiple domains, including public and institutional datasets
- Exploratory data analysis and data visualization capabilities
Responsibilities
- Develop and implement innovative statistical and computational approaches for the analysis of large datasets.
- Stay current on innovations in methods and tools for statistical analyses.
- Participate in the implementation of new tool development for deployment and supports current tools deployed.
- Participates in the design of a project.
- Leads a research effort in the direction set forth by the PI and the specific project.
- Creates reporting specifications for new reports/dashboards/analytical tools and assists in testing/validation;
- Ensures integrity, accessibility, and accuracy of reports/dashboards and data structures;
Other
- Bachelors degree in data science, information science, statistics or related field and 2 years prior work experience in the analysis of genomic, proteomic, and/or clinical data
- Must be authorized to work in the United States without sponsorship
- Ability to disseminate research findings with data visualizations and workflow diagrams
- Prioritizes multiple data and statistical analysis requests effectively
- Meets deadlines for recurring and ad hoc requests
