Biomedical Data Statistician, Technical Assistant

Department

BSD DFI - Translational Informatics

The Duchossois Family Institute (DFI) at the University of Chicago is using cutting‑edge technologies and developing facilities to accelerate research and introduce novel interventions that optimize microbiome‑mediated host defenses, with the goal of enhancing health. By developing new knowledge about the interactions between the human body and the microbiome, the DFI is breaking new ground that will lead to therapies that increase resistance to a wide range of diseases.

The job provides basic technical support activities related to the statistical design and implementation of research analysis. Uses knowledge of statistics to communicate analyses for research proposals and draft reports detailing their results. Follows objectives and procedures for sampling, randomization, data collection, analysis, and quality control. The Duchossois Family Institute (DFI) is seeking a part‑time Biomedical Data Statistician Technical Assistant to support the development of scalable analytical applications for managing and analyzing large biomedical datasets.

Working within the Translational Informatics team, the successful candidate will leverage modern technologies to organize, integrate, and extract insights from complex data. Ideal candidates will have experience with web frameworks, APIs, databases, data visualization tools, and back‑end programming languages, as well as the ability to contribute across the technology stack.

• Apply foundational knowledge in probability, variance, hypothesis testing, regression analysis, experimental design, and statistical programming languages, particularly R and Python (including libraries like pandas or Num Py).
• Utilize SQL skills for querying, cleaning and merging large datasets from relational databases.
• Assist in the development, testing, and optimization of prompts to extract structured, accurate data from Large Language Models, working collaboratively under direct supervision.
• Select, transform, and create data variables to improve the accuracy of predictive models.
• Assist to create insightful dashboards and charts using visualization tools such as Tableau or Power BI.
• Contribute to the development of data algorithms and specialized computer software for identifying and classifying components of biological systems (such as DNA and protein sequences).
• Support the creation and maintenance of data models, databases, and software applications.
• Assist in the production, presentation, and discussion of high‑quality data analysis reports for audiences.
• Participate in the dissemination of research findings through scientific publications.
• Relies on administrative guidance and review, collaborates with researchers in designing, implementing, and monitoring research.
• Assist in developing objectives, designing sampling, randomization, and data collection procedures to achieve study objectives.
• Assist in analyzing data and preparing tables for interim and final reports. May help design tables and statistical procedures for specific research studies.
• Maintain collaboration with other research support personnel, including analysts and programmers in monitoring and assessing data quality.
• Perform other related work as needed.

Education: Minimum requirements include vocational training, apprenticeships or the equivalent experience in related field (not typically required to have a four‑year degree).

Work Experience: Minimum requirements include knowledge and skills developed through < 2 years of work experience in a related job discipline.

• Practical knowledge of supervised and unsupervised learning algorithms such as decision trees, random forests, and clustering.
• Experience working with biomedical and computational datasets to build custom inward and outward facing applications with advanced AI/ML approaches and robust visualization.
• Experience building large‑scale web applications and using statistical packages/software to analyze multi‑modal microbiome research data.
• Hands‑on experience implementing supervised and unsupervised machine learning…