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BSD OBG - Lengyel Lab

About the Department

The Kenny/Lengyel laboratory is part of the Department of Obstetrics and Gynecology/ Section of Gynecologic Oncology, studying the biology of ovarian cancer. The laboratory has about 15 members investigating the role of metabolism and methyltransferases in ovarian cancer metastasis. We use a variety of cutting-edge methods, including spatial proteomics, spatial metabolomics, 3D organotypic cultures of human tissue, spatiotemporal characterization of the immune system, and stable-isotype tracing in patients. Bioinformatic support and access to all Core facilities at the University of Chicago are available in the laboratory.

Our translational research laboratory is in the Center for Integrated Science, a research building on campus that houses 40 independent research groups.

This at-will position is wholly or partially funded by contractual grant funding which is renewed under provisions set by the grantor of the contract. Employment will be contingent upon the continued receipt of these grant funds and satisfactory job performance.

Job Summary

Under direct supervision, this job performs a broad range of operational activities, which may include collecting, organizing, and analyzing information from the University\'s various internal data systems as well as from external sources. This job also performs assignments related to data manipulation, statistical applications, programming, analysis and modeling.

The Ovarian Cancer Research Lab at the University of Chicago is seeking a full-time, on-site Clinical Data Scientist/Analyst to support multiple research projects with an emphasis on image analysis and computational pathology and the development of AI-driven data pipelines. The ideal candidate is a motivated individual with strong programming skills, a passion for innovation, and an interest in applying computational methods to translational cancer research.

This at-will position is wholly or partially funded by contractual grant funding which is renewed under provisions set by the grantor of the contract. Employment will be contingent upon the continued receipt of these grant funds and satisfactory job performance.

Responsibilities

• Design, implement, and maintain AI/ML pipelines in support of ongoing research projects.
• Analyze large-scale data (e.g., digital pathology slides) using standard AI/ML libraries (e.g., PyTorch, TensorFlow).
• Contribute to image processing and algorithm development to support the identification of novel biomarkers and disease phenotypes.
• Write clean, efficient code primarily in Python and work with Bash/Slurm scripting as needed; as we utilize distributed computing resources to accelerate model training and large-scale data analyses.
• Collaborate with team members to optimize compute workflows and troubleshoot technical bottlenecks.
• Assists in analyzing data for the purpose of extracting applicable information. Performs research projects that provide analysis for a number of programs and initiatives.
• May assist staff or faculty members with data manipulation, statistical applications, programming, analysis and modeling on a scheduled or ad-hoc basis.
• Collects, organizes, and may analyze information from the University\'s various internal data systems as well as from external sources.
• Maintains and analyzes statistical models using general knowledge of best practices in machine learning and statistical inference. Performs maintenance on large and complex research and administrative datasets. Responds to requests and engages other IT resources as needed.
• Performs other related work as needed.

M nimum Qualifications

Education:

Minimum requirements include a college or university degree in related field.

Work Experience:

Minimum requirements include knowledge and skills developed through

Certifications:

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Preferred Qualifications

Education:

• A degree in Computer Science, Bioinformatics, Engineering, or a related field.

Experience:

• Experience in a research or lab setting is an advantage but not mandatory.

Preferred Competencies

Strong coding background, preferably proficiency in Python programming.

Enthusiasm for applying AI/ML techniques in a research environment.

Familiarity with machine learning libraries or frameworks such as PyTorch, TensorFlow, experience with image analysis, especially in a biomedical context or even exposure to bioinformatics tools or pipelines would be a plus. As would be an understanding of hands-on experience with high-performance computing resources (e.g., Slurm).

Strong problem-solving skills and the ability to work independently on complex research tasks.

Strong attention to detail.