Data Analyst I, Department of Medicine

University of Texas at Austin-posted about 2 months ago
Full-time • Entry Level
Austin, TX
251-500 employees
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The Department of Medicine’s Division of Oncology at the Dell Medical School is seeking a Data Analyst I. 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.
  • Technical Learning Learns new data tools and platforms with minimal guidance. Quickly adapts to changes in data systems or reporting requirements. Applies new statistical methods or visualization techniques to improve analysis.
  • Detail Orientation Ensures data accuracy before publishing data Documents assumptions and methodologies clearly. Reviews peer work for quality assurance.
  • Time Management Prioritizes multiple data and statistical analysis requests effectively. Meets deadlines for recurring and ad hoc requests. Allocates time for both reactive and proactive analysis.
  • Statistical Methods Regression & GLMs – Fits linear/logistic/Poisson models; checks assumptions and interprets effects. Classification & Diagnostics – Evaluates ROC- and PR-AUC, calibration, and threshold trade-offs. Resampling & Validation – Uses bootstrap/permutation; applies k-fold/nested cross-validation. Survival Analysis – Builds KM curves, log-rank tests; fits Cox PH and verifies assumptions. Enrichment Analysis – Performs GSEA; uses hypergeometric/Fisher tests with FDR control. Clustering – Applies k-means/hierarchical; evaluates with silhouette; uses PCA/UMAP for structure. Mixed/Hierarchical Models – Models random effects for clustered/repeated measures; reports ICC. Nonparametric Methods – Applies rank-based tests (Wilcoxon, Kruskal–Wallis, Spearman/Kendall). Time Series & Forecasting – Analyzes trend/seasonality; fits ARIMA/ETS and backtests accuracy. Power & Sample Size – Computes requirements for t-tests/ANOVA/regression/survival.
  • 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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