Environmental Data Validation Chemist
$60k - $85kMRA Recruiting Services
Technical requirements:
- 7+ years of experience in environmental chemistry
- Have sufficient technical expertise and knowledge to make scientifically defensible decisions with respect to data usability.
- Communicate and discuss custom reporting requirements with data analysts to generate project-specific tables.
- Strong communication skills, with the ability to interact professionally with internal team members, cross-departmental colleagues, and clients.
- Solid environmental laboratory experience including familiarity with analytical methods.
- Assist in the preparation and revision of Quality Assurance Project Plans (QAPPs), with a focus on analytical methods and data review procedures.
- Demonstrated expertise in multiple levels of data review and validation. While a strong foundation is expected, training will be provided for any methods or matrices with which the candidate is not yet familiar.
- Able to communicate technical scenarios and implications to multiple parties (contractors, laboratories, data analysts, consultants) resulting in resolutions.
- Be knowledgeable in federal, state, or local agency data validation guidelines (such as US EPA National Functional Guidelines)
- Familiarity with EQuIS DQM
- Assist in preparing data validation SOPs for internal use.
- Support the drafting of white papers explaining technical subjects of potential interest to existing and potential clients and colleagues.
- Contribute to the development of automation tools and demonstrate a willingness to incorporate existing ddms automation solutions into daily workflows.
- Further self-learning and guidance from others, while providing guidance to advance the competencies of the entire department.
- Contribute towards building and continually improving ddms’ technical training programs. ddms training should emphasis accuracy, technical validity, standardization, and error reduction.
- Leverage technology to identify, communicate, and help eliminate inefficiencies in workflows, contributing to continuous improvement across projects.
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