Associate Data Scientist, Spatial Data

About the position

The primary purpose of the Associate Data Scientist is to work as a computational scientist in the laboratory of Dr Kunal Rai, in Genomic Medicine Department at The University of Texas MD Anderson Cancer Center, Houston, Texas. The position is focused on discovery of cancer-driving epigenome-networks by applying computational methods for the analysis of single cell multiome and spatial transcriptomics and epigenomics (ATAC-Seq/CUT&Tag) datasets and integrate with complex high-dimensional omics datasets from tumor samples in different cancer types/malignancies. This position will also collaborate with a group of wet lab and computational scientists to derive novel biological insights and identify biomarkers and therapies. Rai laboratory is situated in a highly dynamic and stimulatory environment for learning. MD Anderson Cancer Center is a top-rated hospital in cancer care in the United States. The institute offers active graduate and postdoctoral training programs and the unmatched scientific environment of the Texas Medical Center, the world's largest biomedical center.

Responsibilities

• Carry out preparation, clean-up, and quality control of biological data, including scRNA-Seq, scATAC-Seq, Spatial transcriptomics and/or other multi-dimensional omic data modalities.
• Develop and maintain pipelines for bioinformatics and statistical analyses of aforementioned data types; activities to include handling raw data, evaluating outputs, optimizing parameters, and summarizing findings.
• Collaborate with interdisciplinary teams to design experiments and analyze data from various single cell platforms.
• Maintain knowledge of latest bioinformatic approaches and variety sequencing technologies, especially in single-cell context.
• Present results at multidisciplinary project meetings.
• Produce output for scientific publications and co-author said publications.
• Prepare written reports, manuscripts, and grant applications with investigators.
• Work closely with the team and collaborators to discover novel therapeutic opportunities for cancer patients.

Requirements

• Deep knowledge of bioinformatics tools and their implementation as part of pipelines, particularly for scRNA-Seq, scATAC-Seq, Spatial transcriptomics and/or other multi-dimensional omic data modalities.
• Demonstrated experience and understanding of genomic technologies and analysis of data generated.
• Analyzing and interpreting outputs to identify insights and hypotheses from data.
• Understanding of essential statistical methodologies required for bioinformatics analyses.
• Addressing challenges in bioinformatics as well as mitigation strategies such as bias, batch correction, etc.
• Utilizing High Performance Computing to run large-scale analyses.
• Unix, R, Python, or other scripting/programming languages.

Nice-to-haves

• PhD in natural/biological science, computational, bioinformatics.
• Computational biology experience (at least 1 or 2 projects) working in a lab or industry.
• Experience with R and Python.
• Experience with Machine learning/Deep learning OR single cell data OR spatial data analysis.
• Experience with Spatial transcriptomics data analysis.
• Experience with Epigenomics (ChIP-Seq, RNA-Seq, CUT&Tag, Hi-C) data analysis.
• At least 1 paper in bioinformatics/comp biology.

Benefits

• Referral Bonus Available
• Relocation Assistance Available