Research Data Analyst - Internal Medicine (Pulmonary)

Generate, Manage, and Process Epigenomic Data:

Support the generation, preprocessing, and quality control of high-throughput sequencing datasets, including Whole Genome Bisulfite Sequencing (WGBS). Apply knowledge of DNA methylation chemistry and bisulfite conversion while utilizing R or Python (e.g., Bioconductor) and maintaining reproducible workflows with Snakemake or Nextflow and version control (Git).

Analyze Single-Cell and Multi-Omics Data:

Perform analysis of single-cell sequencing datasets (e.g., scRNA-seq) and integrate these with DNA methylation profiles to investigate cell-specific regulatory mechanisms. Use tools such as Seurat or Scanpy and apply dimensionality reduction techniques (e.g., UMAP, PCA) for high-dimensional data analysis.

Execute Analytical Workflows and Ensure Data Integrity:

Run and maintain established bioinformatics pipelines, ensuring accuracy, reproducibility, and consistency of results. Document analytical methods, parameters, and outputs in accordance with best practices.

Perform Statistical Analysis and Biomarker Identification:

Apply statistical inference and machine learning methods to analyze complex genomic datasets and identify epigenetic patterns associated with disease. Utilize public genomic resources such as TCGA, ENCODE, and GEO as appropriate.

Validate Findings Across Datasets:

Assist in validating results across independent cohorts and platforms by implementing robust and reproducible computational workflows.

Data Visualization and Communication:

Generate clear, publication-quality visualizations (e.g., heatmaps, Manhattan plots, UMAPs) and communicate findings to research team members, supporting manuscripts, presentations, and reports. Six (6) years of experience in Biostatistics, Statistics, Bioinformatics, Mathematics, Computer Science, Computational Biology, or related field. Education may be substituted for experience on a year for year basis. Experience on analysis of large epigenomics and single-cell datasets. Two (2) years' experience with cloud or high-performance computing environments (AWS, GCP, Azure, or institutional clusters). Prior contribution to peer-reviewed publications as evidenced by application materials. Scientific Communication Time Management Problem-Solving Resume/CV Cover Letter https://www.kumc.edu/human-resources/benefits.

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