Associate Data Scientist - Gynecologic Oncology

The Gynecologic Oncology and Reproductive Medicine department at MD Anderson's Cancer center is internationally renowned for research-driven patient care, specializing in the medical and surgical treatment of all types of gynecologic cancers including ovarian, vaginal, vulvar, endometrial and cervical cancers, as well as malignant ovarian germ-cell tumors, ovarian sex-cord stromal tumors, borderline tumors of the ovary, cervical intraepithelial neoplasia, human papillomavirus and gestational trophoblastic disease.

The ideal candidate e xperience in analyzing spatial biology datasets (proteomics, metabolomic and transcriptomics).

Salary range:
Minimum $88,000.00 - Midpoint $ - Maximum $

• Assist in the analysis, design and development of pipelines, algorithms and software for the analysis, exploration, and visualization of data generated using exome/whole genome sequencing, RNA-seq, ChIP-seq, ATAC-sequencing, chromatin conformation capture sequencing (e.g. Hi‑C and similar methods) and spatial transcriptomics in a timely fashion.
• Apply statistical and machine learning techniques to derive insights from complex, high‑dimensional datasets, including in‑house and public data (e.g., TCGA, CCLE, GTEx, GEO).
• Run workflows for somatic mutation calling, copy number analysis, transcriptional profiling, clonality estimation, and mutational phylogeny inference on matched tumor/normal and longitudinal samples.
• Run workflows for protein and metabolites analyses and integrate these datasets with above mentioned transcriptomics profiles.
• Independently plan and adapt novel computational approaches to support evolving research needs and emerging experimental technologies in genomics, spatial, and single‑cell omics.

• With direction, develops and maintains bioinformatics tools and infrastructure using Python, R, Docker, Conda and other tools; create well‑organized, reusable, and user‑friendly workflows to support lab‑wide analyses.
• Generate custom visualizations and perform downstream analyses including gene expression profiling, mutation analysis, gene set enrichment, pathway analysis, protein and metabolomic analyses.
• Ensure reproducibility and transparency by organizing data, documenting code, version‑controlling pipelines, and maintaining clear scientific records in notebooks and shared repositories.
• Contribute to the implementation of robust data stewardship practices and help maintain a scalable, collaborative analysis environment leveraging high‑performance computing (HPC) systems.

• Collaborate with experimental biologists, wet lab members, data scientists, and cross‑functional institutional teams to integrate omics data with biological and clinical datasets and harmonize metadata across studies.
• Assist with the preparation of manuscripts, figures, abstracts, grant proposals, and internal reports as well as for presentation at lab meetings, departmental reviews, and institutional events.
• Communicate complex analytical findings effectively to both technical and non‑technical audiences through presentations, visual summaries, and written reports.
• Conduct literature reviews and stay current with emerging tools and trends in genomics, computational biology, and biomedical data science to inform pipeline development and project planning.
• Support mentoring and onboarding of students, fellows, and research personnel by contributing to training in computational methods and analytical workflows.
• Performs other duties as assigned.

Education Required:

Bachelor's degree in Biomedical Engineering, Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Statistics, Computer Science, Computational Biology, or related field.

Experience

Required:

Two years experience in scientific software or industry development/analysis.

Experience Preferred:
Experience in analyzing spatial biology datasets (proteomics, metabolomic and transcriptomics).

The University of Texas MD Anderson Cancer Center offers excellent benefits, including…