Job Overview
This role involves ensuring quality across the full lifecycle of scientific products and solutions in a remote environment. You will partner with scientists and technical teams to validate requirements, review documentation, ensure compliance with quality standards, translate scientific needs into testable requirements, manage verification and validation activities, support audits, and drive continuous improvement.
Responsibilities
- Work with stakeholders to facilitate technical coordination during the transition to operations phase.
- Plan tasks and align transition activities to project schedules and needs.
- Gather requirements and prepare Requirements Allocation Documents, ensuring complete traceability.
- Identify gaps, track actions, and coordinate across teams to mitigate risks.
- Prepare presentations and lead lifecycle reviews.
- Ensure compliance with established quality assurance standards and procedures during development and packaging.
- Support Agile-based software development efforts, participating in sprint planning, reviews, and retrospectives.
- Conduct lifecycle audits and risk assessments to identify gaps and recommend corrective and preventive actions.
- Collaborate with scientists, engineers, and project managers to ensure lifecycle documentation is complete, traceable, and compliant.
- Provide subject matter expertise on quality standards, validation processes, and best practices.
- Mentor and train junior QA staff and contribute to the development of quality management systems.
- Review and analyze scientific data, protocols, and documentation for accuracy, validity, and adherence to regulatory requirements.
Qualifications
- Bachelor’s degree in a technical field (e.g., Engineering, Computer Science, Information Technology) with 4–8 years of experience in quality assurance, scientific analysis, or lifecycle management; or minimum of 6+ years of directly related experience in lieu of a degree.
- 2+ years of experience developing or integrating software for scientific applications (preferably Fortran/C/C++ in a Linux environment) to assess algorithm complexity and required effort.
- Knowledge of software development lifecycles and Agile/DevOps methodologies.
- Demonstrated experience with quality management systems, risk assessments, and lifecycle documentation.
- Experience working in cross-disciplinary scientific or engineering teams, including collaboration with scientists, engineers, and project managers.
- Proficiency in data analysis tools and scientific software.
- Excellent written and verbal communication skills with the ability to translate complex scientific findings into clear recommendations.
- Strong analytical and problem-solving skills with the ability to troubleshoot complex technical issues.
- Strong attention to detail, planning, and organizational skills.
