Job Summary:
The Data Science Institute (DSI) is excited to be a part of the UW-Madison's Research, Innovation and Scholarly Excellence (RISE) Initiative, which is dedicated to addressing significant, complex challenges through interdisciplinary collaboration. RISE-AI will bring up to 50 new faculty and more than double campus investment in AI and related fields. RISE-EARTH and RISE-THRIVE are similar initiatives emphasizing sustainability and health. To support the RISE initiative, DSI aims to significantly expand our existing team of data scientists and research software engineers, with particular emphasis on AI engineering support, MLOps, and data engineering.

DSI is leading a vibrant, innovative, inclusive, and collaborative data science research community. As a member of our highly-valued team of data scientists, software engineers, AI engineers, and MLOps specialists, you will be key to our success. DSI staff partner with domain scientists across a wide range of topics and directly participate in research projects. In addition to supporting the research agendas of RISE faculty, our staff are encouraged to help facilitate collaborations and shape research projects in their early stages. Self-directed research is possible if mechanisms can be identified to support that work, and if it aligns with the mission and vision of DSI. In addition, our staff are expected to participate in the programmatic activities of the institute.

We aim to hire four new Research Cyberinfrastructure Specialists, which is a title that can accommodate different working job titles including AI Engineer and MLOps Specialist. We encourage applications from candidates with a wide range of professional and educational backgrounds for these positions, and we will choose titles and level depending on skill set and experience.

Find out more about the RISE initiative at: https://rise.wisc.edu/
Find out more about the Data Science Institute at: https://dsi.wisc.edu/

Responsibilities:
Research Cyberinfrastructure Specialist I (RE073)
Research Cyberinfrastructure Specialist II (RE074)
Research Cyberinfrastructure Specialist III (RE075)
Institutional Statement on Diversity:
Diversity is a source of strength, creativity, and innovation for UW-Madison. We value the contributions of each person and respect the profound ways their identity, culture, background, experience, status, abilities, and opinion enrich the university community. We commit ourselves to the pursuit of excellence in teaching, research, outreach, and diversity as inextricably linked goals.

The University of Wisconsin-Madison fulfills its public mission by creating a welcoming and inclusive community for people from every background - people who as students, faculty, and staff serve Wisconsin and the world.

For more information on diversity and inclusion on campus, please visit: Diversity and Inclusion

Education:
Required
Bachelor's Degree
Preferred focus in statistics, computer science, or a related discipline that leverages data science techniques.

Qualifications:
Required:
- Demonstrated experience in one or more of the following languages: Python, R, Julia, or comparable language(s)
- Basic software development skills including experience using Git, GitHub/GitLab and tools for managing packages and computing environments (with tools like venv, conda, pixi)
- Professional experience developing technical documentation, tutorials, training, or other support materials for end users
- Strong oral and written communication skills, with attention to interpersonal relationships and consensus building
- Demonstrated strong work ethic and professionalism, including responsibility, discretion, and reliability
- Strong organizational skills and attention to detail
- Ability to set priorities, exercise initiative, solve problems, adapt to changes quickly, make independent judgments, and work both independently and collaboratively as part of a team

Preferred:
- Proficiency with AI/ML frameworks such as keras, tensorflow, pytorch, and JAX
- Demonstrated experience using distributed computing systems (e.g. cloud, high-performance computing, or high-throughput computing systems)
- Experience with MLOps tools such as weights-and-biases, MLFlow, neptune, etc.