The University of Texas at Austin-posted 8 days ago
Full-time • Entry Level
Onsite • Austin, TX
5,001-10,000 employees

The Department of Medicine’s Division of Oncology at the Dell Medical School is seeking a Data Analyst I. Note: This candidate must be authorized to work in the United Stated without sponsorship. Our cancer research program focuses on translational research models to define molecular alterations associated with the processes targeted by various cancer therapies and use these associations to inform treatment choice in trial design. We use several public domain data sources for obtaining molecular data types and clinical outcomes data from various cancers and apply methods for combining them to extract relevant information. We also use information on cancer cell biology to develop strategies to define molecular susceptibilities that may be targeted by treatment. The Kowalski lab is part of the Department of Medicine’s Division of Oncology. We seek a Data Analyst to carry out computational research in a highly collaborative and interdisciplinary environment with world-class experts and state-of-the-art technologies. This position will carry out computational analyses in the area of cancer clinical genomics. Data Analyst I provides analysis of existing data and data structures and satisfies ad-hoc reporting/analysis requests. 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; reviews and approves user requests for access to reporting data and tools. Consults with faculty and/or staff to identify new business reporting needs and provides guidance and interpretation of complex environments and data. Documents data analysis efforts (data sources, reporting specifications, tools, issue/problem resolutions). Researches and stays up-to-date on emerging technologies and data analysis tools.

  • Develop and implement innovative statistical and computational approaches for the analysis of large datasets. These datasets may utilize several types of available data sources, including public domain.
  • Supports the generation of preliminary results for grant submissions, writes and edits grants and grant progress reports.
  • 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.
  • 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 or Master's degree in a related field and experience in the analysis of genomic, proteomic, and/or clinical data .
  • Relevant education and experience may be substituted as appropriate.
  • 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
  • Previous AI experience with large language model agent orchestration, creation, and usage, as well as with RAG and embeddings.
  • Ability to disseminate research findings with data visualizations and workflow diagrams.
  • Cloud Data Analytics Certification such as: AWS Cloud Practitioner and/or AWS AI Practitioner; Microsoft Certified: Azure AI Fundamentals, Microsoft Certified: Azure Data Fundamentals, and/or Microsoft Certified: Azure Fundamentals
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