Emerald Cloud Lab
Scientific Operations Engineer I
Austin, Texas · Senior
Sponsorship not specifiedDetected 5 hours ago
StatisticsResearchExperimental DesignProblem SolvingCritical ThinkingGxP
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odds of building a lasting career here
37Risky
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- It's an intellectually challenging role, requiring dynamic problem-solving skills, combining scientific knowledge, troubleshooting prowess, and a willingness to engage with problems at all levels.
- A successful member of this team will become familiar with all experiments offered at Emerald, getting to know the software and hardware used to carry out experiments in the cloud lab.
- SciOps are continually driving to improve the productivity and reliability of the lab.
Requirements
- As a SciOp you work extensively with other teams to improve the quality of the cloud lab.
Nice to have
- You might be a great fit for Scientific Operations at ECL if...
- You have 5+ years of experience working in a GxP or Life Sciences environment.
- You have experience applying systemic troubleshooting methodologies to complex technical problems.
- You are able to work fixed, but non-traditional hours.
- A specific night role is also available.
- You are a data driven individual with statistical analysis experience.
- You have worked extensively in the course of research to troubleshoot both experiments and complex experimentation.
- You are a detail-oriented thinker who takes pride in precision and experimentation.
Company info
- Members of the SciOps team require a deep understanding of the code at the interface between customers and the physical laboratory.
- About You We are seeking team members who are excited to reshape how scientific research is done, and who are willing to learn new things and solve the technical challenges that are necessary to make this happen.
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This listing is sourced directly from Emerald Cloud Lab's careers page and normalized into a canonical job model.