Research Data Analyst

Full time @Genius Forest Ventures in Data Analytics
  • Actis, United States, 9464 - 00100 View on Map
  • Post Date : April 18, 2022
  • Apply Before : April 21, 2022
  • Salary: $1,400.00 - $3,000.00 / Monthly
  • 0 Application(s)
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Job Detail

  • Career Level Entry-level
  • Experience 4 Years
  • Industry Data Analytics
  • Qualifications Degree Bachelor

Job Description

The Research Data Analyst will work in microscopy and microscopy data analysis in collaborative research efforts across scientific scopes at UCSF. This position requires direct interaction with researchers as they will perform duties such as assisting in experimental design, documentation, experiment execution, and post-acquisition image and statistical analysis, as well as training users to work independently at microscopes and other Biological Imaging Development CoLab (BIDC) equipment. This individual will work under the direct supervision of the director and will receive mentoring and direct guidance from principal investigators or senior researchers of research projects to obtain data and to advance their career in academic research. The candidate will also contribute to the overall success of the BIDC by working to assist in microscope maintenance, upkeep, and repair. Additional responsibilities include bookkeeping, ordering, maintaining stocks of necessary supplies, and performing other duties as assigned.

The Research Data Analyst works on research data reporting assignments that are of moderate diversity in scope. Exercises judgment within generally defined practices and policies in selecting methods and techniques for obtaining solutions.

Department Description

CoLabs is a new model for research collaborations and core labs for the UCSF community. CoLabs brings together several established and successful but previously separate cores plus new institutional support into a new centralized research organization at the Parnassus campus. CoLabs seeks to revitalize the model for core resources by capitalizing on critical personnel and cutting-edge technologies and driving collaboration across disciplines. CoLabs will centralize research teams and create a set of common pipelines and workflows. It will recruit and retain world-class staff and develop transformative methods and technologies through engagement with the research community and institutional investments.

The CoLabs initiative is launching now with participation of five founding CoLabs teams, Biological Imaging Development (BIDC), Data Science (DS), Disease to Biology (D2B), Flow Cytometry, and Genomics. The CoLabs will help organize and integrate the UCSF community through common pipelines and data curation around a set of collaborative “CoProjects” that will tackle fundamental questions in the biomedical research. CoProject day-to-day operations are managed and run through the CoLabs. Several CoProjects are already underway, with more coming in line soon.

THE BIDC The Biological Imaging Development CoLab is an interdisciplinary center configured to assemble, test, and apply emerging light microscopy techniques and technologies. It is designed to serve two roles: a conduit for new optical imaging technology and as a site for technology development. The CoLab also seeks to provide centralized space, personnel, and technical tools toward advancing optical imaging in the biological sciences.

Required Qualifications

  • Bachelor’s degree in related area and/or equivalent experience/training
  • One or more years of related work experience
  • Working knowledge of research function
  • Working skills in analysis and consultation
  • Skills to communicate complex information in a clear and concise manner both verbally and in writing
  • Working skills in statistical analysis, systems programming, database design and data security measures
  • Proficient in statistical analysis in Python, R, or MATLAB

Preferred Qualifications

  • Hands on knowledge of and skill in using microscopes common to biological research
  • Research experience in microscopy specific image analysis