In addition to evaluating each candidate's relative ability, as demonstrated by quality and breadth of experience, the following factors will provide the basis for competitively evaluating each candidate:
The ideal candidate brings extensive experience applying advanced population modeling and statistical analyses to support the management and conservation of aquatic species, particularly salmonids. They have a demonstrated ability to develop new models, apply existing models to diverse datasets, and interpret outputs to inform operational decisions, conservation strategies, and regulatory compliance. They regularly use statistical software, such as R, Python, SAS, MATLAB, etc., to conduct analyses and interpret results. The candidate integrates long-term monitoring data and field-collected datasets (e.g., species counts, water quality, flow, temperature, and habitat measurements) with field observations to evaluate population trends, quantify abundance, and identify ecological or habitat factors that may limit species recovery. They understand the ecological dynamics of riverine and estuarine systems and are capable of assessing habitat conditions based on structured data collection and analysis to guide restoration and adaptive management efforts. They manage and maintain datasets or databases used for monitoring, modeling, and analysis, and perform QA/QC to ensure data integrity.
In addition to technical expertise, candidates should have a strong background in designing and implementing monitoring programs, surveys, or field experiments, including establishing metrics, indicators, and data management protocols that support rigorous, reproducible analyses. They can synthesize results from multiple data sources (monitoring datasets, modeling outputs, and literature reviews), conduct literature reviews, and translate complex findings into actionable recommendations for internal leadership, interagency partners, and regulatory stakeholders through written technical reports, written summaries, and formal presentations. They mentor, train, or provide technical guidance to staff or collaborators in data collection, analysis, and modeling methods. The ideal candidate demonstrates experience working in collaborative, cross-disciplinary, and matrix-managed environments and fosters a culture of analytical rigor and scientific best practices, ensuring the quality, defensibility, reproducibility, and QA/QC of all technical products.
The successful candidate also has experience applying population viability and risk modeling, evaluating the effects of water operations on species and habitats, and using GIS and spatial analysis to inform management decisions. They can manage large datasets, interpret multi-year monitoring results, and contribute to adaptive habitat restoration projects while coordinating projects and analyses with external agencies or partners. They apply experimental design principles, conduct peer review and quality assurance, and translate complex technical findings into actionable recommendations communicated to supervisors, management, and external regulatory or stakeholder audiences.
