Job Description

Massey Cancer Center

“To improve the lives of all Virginians by delivering cutting-edge cancer care through patient-centered prevention and treatment; high-impact, innovative research; community input and engagement; and education and training of the next generation of researchers and healthcare professionals.”

Virginia Commonwealth University (VCU) Massey Cancer Center is a nationally ranked cancer center located in the heart of Downtown Richmond. All full-time staff are eligible for our generous benefits package that includes choices for health, vision, and dental coverage, life-insurance, short and long-term disability coverage, retirement planning, tax-deferred annuity and cash match programs, flexible spending accounts, tuition benefits, significant paid-time off,12 paid holidays, and more. Explore our benefits further here: https://hr.vcu.edu/current-employees/benefits/university-and-academic-professional-benefits/

Chief Purpose of this position:
The data scientist position is focused on Computational Biology/Bioinformatics/Data mining in
the area of lung cancer research and will work closely with Dr. Robert Winn and his lab at VCU
Massey Cancer Center. The candidate must have a proven track record in Computational
Biology/Bioinformatics/Data mining as it relates to genome scale research including differential
gene expression, alternative mRNA splicing, global DNA methylation, epigenetic regulation, and
pathway analysis. The research focus of Dr. Winn’s lab is to understand the basic biological
mechanisms of lung cancer initiation and progression and lung cancer health disparities.

Position responsibilities:
Overall Research - Work closely with Dr. Winn and his team using innovative data science
techniques to better understand the genetics and cellular biology of lung cancer cells and
tumors as it relates to the following:

Precision cancer medicine - Create predictive models to predict cancer vulnerabilities
from genomic profiles of tumors and cancer cell lines.

Cancer target identification - Integrate functional screening and ‘omics data to identify
novel cancer targets as well as drug repurposing.

Small-molecule screens - Analyze highly-multiplexed small-molecule screening data to
discover novel cancer therapeutic leads.

Duties
Assist in study design and proposal development.
Develop and implement protocol for quality control.
Create analytic files with detailed documentation.
Select and develop appropriate statistical and bioinformatical tools for addressing a
given research question.
Implement data analysis through statistical and bioinformatical programming.
Present results at weekly lab meetings using graphs and tables.
Develop oral and written dissemination of findings for conference presentations, peerreviewed
journal articles, and grant submissions.
Other duties may also be assigned.

Desired qualifications:
PhD in bioinformatics, computational biology, or similar discipline and at least 2 years’
experience.
Cancer research experience in the fields of bioinformatics, computational biology,
systems biology, epidemiology, proteomics, and/or genomics.
Experience working in a matrix environment within academic medicine.
Proven success in collaborating with multi-disciplinary teams.
Experience working in and fostering a diverse faculty, staff, and student environment.

Preferred qualifications:
Design and analysis of genome research and pathway analysis.
Analysis and interpretation of high-dimensional data, including genomic, transcriptomic,
and proteomic datasets.
RNA-seq and DNA-seq data analysis to generate gene clusters using a combination of
dimensionality reduction analysis (t-distributed stochastic neighbor embedding and
Fuzzy C-means).
Using R and/or Python, Linux, and cloud computing systems to develop robust statistical
methods and algorithms to drive data mining efforts.
Gene Set Enrichment Analysis (GSEA) use to identify initial gene clusters, using Enrichr,
ENCODE, GO, KEGG, to generate signal transduction pathways.

Location
Within greater Richmond area or remote

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