Data Analyst I, Department of Medicine

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Job Details:

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.

• 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.

• Ensures data accuracy before publishing data

• Documents assumptions and methodologies clearly.

• Reviews peer work for quality assurance.

• Prioritizes multiple data and statistical analysis requests effectively.

• Meets deadlines for recurring and ad hoc requests.

• Allocates time for both reactive and proactive analysis.

• 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.

Bachelors degree in data science, information science, statistics or related fieldand 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. 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

$56,000+ depending on qualifications

• Standard office equipment

• Repetitive use of a keyboard

• May be exposed to such occupational hazards as communicable diseases, blood borne pathogens, ionizing and non-ionizing radiation, hazardous medications and disoriented or combative patients, or others.

• Resume/CV

• 3 work references with their contact information; at least one reference should be from a supervisor

• Letter of interest

Importantfor applicants who are NOT current university employees or contingent workers:You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi-select additional files. Before submitting your online job application, ensure thatALLRequired Materials have been uploaded. Once your job application has been submitted, you cannot make changes.

Important for Current university employees and contingent workers:As a current university employee or contingent worker, you MUST apply within Workday by searching for Find UT Jobs. If you are a current University employee, log-in to Workday, navigate to your Worker Profile, click the Career link in the left hand navigation menu and then update the sections in your Professional Profile before you apply. This information will be pulled in to your application. The application is one page and you will be prompted to upload your resume. In addition, you must respond to the application questionspresented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above.

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Employment Eligibility:

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Retirement Plan Eligibility:

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Background Checks:

A criminal history background check will be required for finalist(s) under consideration for this position.

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Equal Opportunity Employer:

The University of Texas at Austin, as an equal opportunity/affirmative action employer,complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.

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Pay Transparency:

The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor's legal duty to furnish information.

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Employment Eligibility Verification:

If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States. Documents need to be presented no later than the third day of employment. Failure to do so will result in loss of employment at the university.

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E-Verify:

The University of Texas at Austin use E-Verify to check the work authorization of all new hires effective May 2015. The university's company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following:

• E-Verify Poster (English and Spanish) [PDF]
• Right to Work Poster (English) [PDF]
• Right to Work Poster (Spanish) [PDF]

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Compliance:

Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP-3031.

The Clery Act requires all prospective employees be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.