Data Analyst , Department of Medicine

Job Posting

Title:

Data Analyst I, Department of Medicine

Hiring Department: Department of Medicine

Position Open To: All Applicants

Weekly Scheduled

Hours:

40

FLSA Status: Exempt

Earliest

Start Date:

Nov 03, 2025

Position Duration: Expected to Continue Until Aug 31, 2029

Location: AUSTIN, TX

General Notes

The Department of Medicine’s Division of Oncology at the Dell Medical School is seeking a Data Analyst I. This candidate must be authorized to work in the United States 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.

Purpose

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.

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,…