Environmental Scientist CW 2026-02

Position Description

The San Francisco Estuary Institute (SFEI) seeks up to two environmental scientists or environmental engineers to join an interdisciplinary team investigating nutrient biogeochemistry and ecosystem responses in San Francisco Bay. This work will be carried out in support of the San Francisco Bay Nutrient Management Strategy (NMS; sfbaynutrients.sfei.org), a multi-year science program studying the impacts of nutrient loads to San Francisco Bay and evaluating potential management actions. Working with regional collaborators, the NMS studies the Bay’s biogeochemistry, water quality, and habitat condition using ship-based sampling, high-frequency in situ sensors, and coupled hydrodynamic-biogeochemical modeling.

The SFEI-NMS team is recruiting for up to two positions. A major focus of the positions will be the analysis and interpretation of large, rich datasets (e.g., multi-year high-frequency mooring datasets; multi-decade water quality data) to gain mechanistic insights into ecosystem dynamics and/or to evaluate the effects of restoration actions and nutrient reductions on water quality.

Primary position responsibilities will include:

• Design observational and synthesis studies to inform scientific and management questions and provide data needed for models, statistical analyses, and predictive tools.
• Analyze datasets using statistical software and open source code (e.g., Python, R suite, Git).
• Synthesize data from multiple lines of evidence, including both observational and modeling studies, to address key nutrient management issues and develop recommendations for mitigating impacts.
• Communicate findings with stakeholders and collaborators.
• Manage projects involving internal staff and external partners and ensure projects meet deliverable timelines.
• Write technical reports, journal articles, and memoranda
• Contributing to and/or leading grant applications
• Develop presentations and/or posters for stakeholder meetings, public meetings and scientific conferences.

If you are excited about this role, but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or one of our other roles.

Qualifications

Required Experience

• PhD, or a Master’s degree and 7 years of experience, or a Bachelor’s degree and 9 years of experience. Relevant fields include environmental science, environmental engineering, oceanography, or another related scientific field. 

Required Skills

• Strong data analysis skills using Python, R, or Matlab, and demonstrated experience applying those skills to analyze and visualize large, complex environmental datasets.
• In-depth expertise in one or more research areas relevant to understanding the processes and factors shaping estuarine water quality including: biogeochemistry, estuarine ecology, environmental engineering, environmental chemistry, environmental physics, oceanography (chemical, biological, physical), or similar fields. Specific expertise areas include (but are not limited to): nutrient biogeochemistry and eutrophication; phytoplankton ecology (including HABs) and molecular ecology; estuarine physics; or remote sensing.
• Excellent communication skills, demonstrated ability to communicate findings clearly in writing and presentations and to synthesize scientific information for general and technical audiences.
• Experience with technical project management, organizational skills, and ability to manage multiple projects at once and meet deadlines.
• Ability to develop and maintain excellent working relationships with diverse groups of external partners and collaborators.
• Ability to work as part of a multidisciplinary team with a collaborative and supportive work approach. 

Preferred Skills

• Post-PhD experience is desirable but not required.
• Experience analyzing long-term monitoring data and/or high frequency environmental datasets (e.g., water quality sensors, ADCPs; in particular in tidal environments).
• Proficiency in statistical methods and data visualization, especially with respect to analyzing large, high frequency time series datasets.
• Experience with data management, quality assurance, and post-processing.
• Experience presenting to and engaging with regulators and regulated entities.