Research Programmer II

About this role

The University of Southern California (USC), founded in 1880, is located in the heart of downtown L.A. and is the largest private employer in the City of Los Angeles. As an employee of USC, you will be a part of a world-class research university and a member of the “Trojan Family,” which is comprised of the faculty, students and staff that make the university a great place to work.

About CSI-Cancer The USC Michelson Center Convergent Science Institute in Cancer (CSI-Cancer) brings together engineers, data scientists, biologists and clinicians to tackle fundamental and translational problems in cancer—from mechanistic modeling and physical dynamics of metastasis to high-content single cell data and advanced machine learning. Our vision is to integrate patient, model system and high‐dimensional data to achieve a mechanistic understanding of cancer dynamics across scales. We are studying circulating tumor cells (CTCs) and other analytes in liquid biopsies (blood, bone marrow aspirates, and other liquids) of cancer patients as an approach to complement cancer care. Our data science driven single-cell multi-omic approach allows us to look at the morphology, proteome, and genome of these CTCs individually.

Position Overview We are seeking a highly motivated Research Programmer II with a strong background in computer science (CS), biomedical engineering (BME), who is comfortable working at the interface of software and hardware with primary emphasis on software and a secondary emphasis on hardware. The successful candidate will join a convergent research team focused on early detection, liquid biopsy, rare-cell detection assays, and the development of novel computational and instrumentation platforms for cancer research. This position is ideal for candidates who want to apply computational science and data engineering expertise to accelerate discoveries in cancer dynamics, trajectory prediction, and treatment response modeling. They will develop and maintain data analysis methods and process pipelines to support the research, data, and analytic infrastructures of the institute ( Home - Convergent Science Institute in Cancer ).

Key Responsibilities • Develop and document robust computational workflows, testing procedures, and production pipelines that support CSI-Cancer’s high-content single-cell, liquid biopsy, imaging, and multi-omics research initiatives. • Supports continuous improvements by maintaining currency with new technologies and leveraging the latest industry knowledge to continually develop skills, knowledge, and abilities. • Serve as a technical resource to researchers, assessing computational requirements and aligning advanced computing solutions (HPC, cloud, containerized environments) with cancer research objectives in mathematical oncology, disease modeling, and translational science. • Support collaborative research efforts at institutional, regional, and national levels to advance CSI-Cancer’s strategic priorities in cancer detection, trajectory forecasting, and precision oncology. • Participate in research projects by contributing to study design, developing prototype computational implementations, and supporting the preparation of technical reports, manuscripts, and grant proposals. • Design, deliver, and maintain training programs and workshops that enable researchers and academic programs to optimally leverage high-performance and cloud computing for cancer research applications. • Partner with interdisciplinary teams of engineers, computational scientists, biologists, and clinicians to implement scalable, reproducible, and compliant research computing solutions while continuously improving processes and technical standards. • Utilize creativity and innovation to solve complex computational challenges arising from cutting-edge cancer research, and identify opportunities to adapt methods and results to novel scientific applications and future research directions. • Promote a collaborative and inclusive research environment consistent with USC’s Code of Ethics. • May supervise junior staff and mentor trainees. • Perform other related duties as assigned or requested. The university reserves the right to add or change duties at any time.

Preferred Qualifications • Deep understanding of HPC, high-performance networking, cloud computing, container technologies (e.g., Docker, Singularity), and scalable research infrastructure.

• Experience supporting computational research in a higher education or research institute setting, particularly for data-intensive scientific projects. • Ability to translate complex scientific research needs — including cancer biology, multi-omics, imaging, and clinical data analysis — into effective computational and analytical solutions. • Proficiency in database systems, version control (e.g., Git), workflow management, and large-scale application performance engineering and optimization. • Experience developing and delivering user training, workshops, or educational programs.

Minimum Qualifications • A Master’s degree in Computer Science, Biomedical Engineering, or a closely related discipline. • Strong software development skills (e.g., Python, C++/C, R, Java, or comparable) and familiarity with data science/machine learning frameworks. • Experience with large, complex biological or clinical datasets. • Demonstrated understanding of advanced computational methods such as contrastive deep learning. Knowledge of current data modeling tools and various machine learning techniques and algorithms (e.g., clustering, decision-tree learning, artificial neural networks). • Proficient use of query languages (e.g., SQL, MDX) and experience working with relational (e.g., MySQL, PostgreSQL) databases. Knowledge of statistical concepts and techniques (e.g., distributions, statistical testing, regression).

• Demonstrated knowledge of the research process, including proposal development, data lifecycle management, reproducible workflows, and compliance with

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