Quantitative Ecological Scientist; Ph.D

Quantitative Ecological Scientist (Ph.D.)

Job :

US-WA-Bellevue

Ecological and Biological Sciences

Full-time

USD $ - USD $ /Yr.

On-Site

Exponent is the only premium engineering and scientific consulting firm with the depth and breadth of expertise to solve our clients' most profoundly unique, unprecedented, and urgent challenges.

Our vision is to engage multidisciplinary teams of science, engineering, and regulatory experts to empower clients with solutions that create a safer, healthier, more sustainable world. For over five decades, we've connected the lessons of past failures with tomorrow's solutions to advise clients as they innovate technologically complex products and processes, ensure the safety and health of their users, and address the challenges of sustainability.

Join our team of experts with degrees from top programs at over 500 universities and extensive experience spanning a variety of industries. At Exponent, you'll contribute to the diverse pool of ideas, talents, backgrounds, and experiences that drives our collaborative teamwork and breakthrough insights. Plus, we help you grow your career through mentoring, sponsorship, and a culture of learning. Thanks for your interest in joining our team!

• 950+ Consultants
• 640+ Ph.D.s
• 90+ Disciplines
• 30+ Offices globally

We are currently seeking a Quantitative Ecological Scientist for our Ecological & Biological Sciences Practice in Bellevue, WA. In this role, you will work as part of a team to provide technical support to various environmental projects in the United States and around the world. Projects may involve environmental assessments, restoration projects, resource matters, impacts relating to chemicals and other stressors in the environment, ecological risk assessment, causal analysis, and resource damage assessment.

Candidates are expected to work collaboratively on projects with other members of the Ecological and Biological Sciences Practice and interdisciplinary teams from other Exponent practices, as appropriate, within a fast-paced, multidisciplinary environment. Eligible candidates will be asked to complete a short data analysis task as part of the evaluation of a candidate's qualifications and experience.

• Synthesizing multiple data sources to detect, quantify, and model ecological change and its underlying drivers
• Processing LiDAR and DEM datasets to generate hydro logically-correct terrain surfaces, including watershed boundaries, drainage networks, and flood/erosion assessments
• Conducting spatial analyses (e.g., slope, buffer, overlay, viewshed, risk mapping) for project-specific needs
• Converting tabular datasets (Excel/CSV, field data) into GIS layers with proper attribution
• Assisting in map production and cartographic outputs for reports and client deliverables
• Supporting georeferencing of scanned maps, imagery, or engineering drawings
• Building and maintaining a structured geodatabase for ongoing projects
• Performing and explaining quantitative analyses with complex datasets
• Reviewing and synthesizing technical and scientific literature and reports
• Applying creative and critical thinking to prepare reports and technical presentations, including visualizations of complex datasets and trends
• Developing professional status by authoring peer-reviewed publications, participating in, and presenting at technical conferences and activities of professional societies
• Collaborating with scientists and engineers to ensure analyses align with project goals

• Ph.D. in the area of Biology, Ecology, Fisheries, Wildlife, or related field with a background in Applied Statistics
• Experience in statistical or population modeling using complex datasets, including large and small datasets collected over differing temporal/spatial scales
• Accomplished in R and/or Python (or equivalent), proficient in git (or equivalent), remote sensing (ESRI, GEE), digital elevation models (DEM) & LiDAR processing, hydrological & terrain modeling
• Proficient in spatial, GIS (ArcGIS, QGIS, or similar), including understanding of raster data…