Predoctoral Research Data Analyst

001 University of Rochester

City of Rochester (NY)

Vor Ort

USD 45.000 - 64.000

Vollzeit

14 Tage+

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

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Zusammenfassung

The Department of Public Health Sciences at the University of Rochester seeks a full-time predoctoral research associate (Data Analyst II) to join NIH-funded health economics research. You will collect and organize data, run statistical analyses, and contribute to manuscript writing with potential for co-authorship.

You will use Stata, SAS, R, or Python to manipulate complex datasets, create maps linking spatial health data, and collaborate with a multidisciplinary team to advance project goals.

Qualifikationen

  • Bachelor's degree in statistical analysis, data base management, or related discipline (required).
  • 1 year experience with data analysis or CRM systems (required).
  • Strong attention to detail and problem-solving skills.
  • Advanced knowledge of Excel; proficient in statistics and data analysis.
  • Programming experience in Stata; willingness to learn SAS, Python, R, or SQL.

Aufgaben

  • Collect and organize data for NIH-funded research projects.
  • Conduct statistical analyses on large databases using Stata, SAS, R, or Python.
  • Contribute to manuscript writing with potential for co-authorship.
  • Create maps and link spatial data to health data.
  • Collaborate with the HEEL team and external collaborators.

Kenntnisse

Statistical analysis
Data management
Data visualization
Programming

Ausbildung

Bachelor's degree in statistics

Tools

Stata
SAS
R
Python
SQL
Excel
GIS

Jobbeschreibung

The Department of Public Health Sciences at the University of Rochester seeks a full-time predoctoral research associate (Data Analyst II) to join NIH-funded health economics research. You will collect and organize data, run statistical analyses, and contribute to manuscript writing with potential for co-authorship.

You will use Stata, SAS, R, or Python to manipulate complex datasets, create maps linking spatial health data, and collaborate with a multidisciplinary team to advance project goals.

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