Job description

Working title: Data Analytics Analyst/Trainer
Salary: Commensurate with experience, excellent benefits


Oregon State University (OSU), University of Oregon (UO), and Portland State University (PSU) are establishing an interdisciplinary cross-institutional team of data science analysts/trainers to conduct data integration, multi-modal data analytics, and machine learning research and training. There will be four analyst/trainers each with expertise in one or more of these three areas and collectively spanning all three areas; two at OSU, one at UO and one at PSU. These positions will collaborate with, assist, and train scientists of all levels in data-intensive research in the life, health, earth, physical and marine sciences.

Two positions will be located in the Center for Genome Research and Biocomputing at Oregon State University and one each in the Data Science Initiative at the University of Oregon and in the Center for Life in Extreme Environments at Portland State University.

Positions in this team are currently open at Oregon State University and at the University of Oregon.

Appointees will jointly serve researchers at the three institutions, as well as smaller institutions across Oregon and southern Washington. They will nucleate and integrate a wider team of data analytics professionals to support each other, connect to relevant expertise, and promote cross- institutional collaboration. They will provide consulting (project planning; proposal assistance), data analysis, teach training courses, and arrange colloquia to inform and instruct faculty, staff and students. This position will contribute to advancing the diversity, equity and inclusion goals of each university. Appointees are required to collaborate in a collegial and inclusive fashion with university staff and clients in the execution of all job functions.

Position duties:

40% Data Analytics training

• Implement multi-campus training program consisting of short workshops and classes (3 days to 5 weeks) in topics in data analytics, including:

    - Data fundamentals: a foundational course that covers good data and software practices, including data privacy and cyber security.

    - Data integration: transforming data held in different sources making it more valuable and presenting users with a unified view. Discovery, access, preparation, aggregation, curation, transformation, semantic harmonization, and sharing.

    - Multimodal data analytics: analytics across heterogenous, high dimensional data sets, using structured data. Data aggregation, model building and uncertainty quantification.

    - Machine Learning: approaches such as differential programming, deep learning, or artificial intelligence.

  • Develop and implement new workshops and classes in other related topics, as desired by the three institutions and research community served by the positions

  • Incorporate distance learning methodologies, webinars, online materials, or other cutting-edge educational solutions where needed, to provide a high quality of service for learners

  • One-on-one training activities specific to individual learner needs

  • Organize cross-institutional user group meetings and other community-based efforts

50% Collaborative research

• Plan and execute research proposals and funded projects with collaborators in the areas of data integration, multimodal data analytics, and machine learning. Analyze data and prepare reports from this research for publications and research proposals. Present research at conferences.

10% Community outreach

• Engage regional institutions in the community of practice, including smaller institutions and underserved communities of users, through summer research opportunities, training of faculty and service providers, collaborative curricular development, and online participation in community activities.





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