Research Data Analyst for Fest Flow Core (Oncology) Johns Hopkins University
Johns Hopkins University6 months ago
Baltimore, Maryland, United States
On-site
Full-time
Junior Level (1-3 years)
Job Description
Position Overview
We are seeking a Research Data Analyst who will provide data analysis and related activities for various types of research projects and studies. You will contribute to the development, maintenance, and use of a research data pipeline that collects and analyzes data. Job Details include: Classified Title: Research Data Analyst; Job Posting Title: Research Data Analyst for Fest Flow Core (Oncology); Role/Level/Range: ACRP/04/MC; Compensation: Starting Salary Range: $48,000 - $84,100 Annually ($66,000 targeted; Commensurate w/exp.); Employee Group: Full Time; Schedule: Mon-Fri 7.5 hours; FLSA Status: Exempt; Location: School of Medicine Campus; Department: SOM Onc Research Laboratory Services; Personnel Area: School of Medicine.
Key Responsibilities
- Collect data and generate reports and models.
- Run models, analyze model results, and prepare reports on the analysis.
- Conduct analyses in support of the assigned research project or study.
- Conduct statistical analyses utilizing standard and statistical software packages.
- Design and prepare tables to illustrate analytic findings.
- Manage or contribute to the entry of data in the assigned database.
- Engage in other research related responsibilities as needed.
- Perform additional duties as assigned.
Required Qualifications
- Bachelor's Degree in a related field.
- Three years of related experience (additional education may substitute for required experience as per the JHU equivalency formula).
Preferred Qualifications
- Experience in single cell transcriptomics or high dimensional -omics data analysis.
- Knowledge of or experience in cancer immunology.
- Experience analyzing spatial transcriptomics data.
Required Skills
data pipeline development
report generation
single cell transcriptomics
spatial transcriptomics
statistical analysis
data analysis
modeling
cancer immunology