Computational Ecologist (SUPP SCIENTIST II)
Consolidated Safety Services - Silver Spring, MD

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Join an experienced scientific team working at the NOAA National Centers for Coastal Ocean Science (Biogeography Branch) to conduct cutting-edge marine ecological predictive analyses.
  • Map, characterize, assess, and model the spatial distributions and movements of marine organisms across habitats throughout the United States and Island Territories
  • Use predictive statistical models, analyze extensive wildlife survey and oceanographic databases
  • Develop, implement, and run machine-learning models for predictive spatio-temporal modeling of marine bird and groundfish distributions to support marine planning processes.
  • Conduct predictive modeling of deep sea corals, marine mammals, sea turtles, marine fish, fishing fleets, and marine ecosystem processes
  • Provide statistical, computational, and analytical support to projects that conduct large-scale ecological and oceanograhic studies
    • Develop spatially-explicit maps and analyses that answer questions of marine management and conservation relevance
    • Design and implement spatial and spatio-temporal statistical models of marine species’ distributions (e.g., seabird and marine mammal occurrence probability and abundance), marine habitat, and marine ecosystem properties
    • Develop and maintain computer code to interface with large oceanographic and ecological databases and mine these databases to improve predictive models
    • Assess model performance and uncertainty in management-relevant scenarios
    • Assist with writing journal articles/reports and present at scientific conferences
    • Offer technical guidance for selection and implementation of different statistical methods to detect patterns in wildlife surveys.
    • Explain statistical results as they relate to project goals and summarize results in the form of tables, figures, journal articles and technical reports.
    • Travel to federal and state laboratories and academic institutions as part of collaborative research projects
  • Develop, maintain, and grow a codebase for advanced spatial analysis
    • Apply new developments in statistical modeling to a marine ecological/wildlife survey context
    • Implement model selection, assessment, and validation algorithms
  • Develop, maintain, and grow oceanographic and ecological geo-databases
    • Build a database of oceanographic and environmental predictor variables of relevance to marine ecological modeling
    • Analyze satellite and observational datasets and raw ocean model outputs to develop derived products that improve predictive models
    • Automate data acquisition, data mining, model assessment & QA/QC processes

    Minimum Qualifications:
    • Experience or academic training in quantitative ecology, advanced statistical modeling, computational analysis, and scientific programming in R and Matlab
    • Demonstrated interest and experience in advanced spatial analysis
    • Advanced degree (Masters or PhD), or equivalent experience, in Quantitative Ecology, Applied Statistics, or similarly highly quantitative field. Ecology, Marine Science and related advanced degrees also acceptable with demonstrated evidence of a strong quantitative focus and statistical and computer programming expertise described below
    • Must be proficient and highly experienced with R and Matlab (3-5+ years experience with one or both of these languages); a code sample may be requested to demonstrate proficiency
    • Experience implementing a variety of spatially-explicit statistical models in R and/or Matlab, including at least 3 of the following: machine learning models (e.g., component-wise boosting), geostatistical models, GLMMs, GAMs, regression trees/forests
    • Ability to independently identify, analyze and solve complex statistical and computational problems
    • Demonstrated written and oral scientific communication skills
    • Able to work effectively in a dynamic, fast-paced, team-oriented multi-project environment
    Preferred Qualifications:
    • Experience with spatial analysis of wildlife survey data, especially marine bird data, in the marine environment
    • Knowledge of ecology, marine science, oceanography, and/or a related field
    • Ability to interface with large databases through THREDDS/ERDDAP servers in R and/or Matlab
    • Experience working with ocean remote sensing data, numerical ocean model outputs (e.g., ROMS), and large distributed oceanographic databases
    • Proficiency with programmable GIS (e.g., Python scripting with ArcGIS or equivalent); Experience with geostatistics (gstat, ESRI Geostatistical Analyst, rgeos, GSLIB, or equivalent)
    • Although not required, we value experience developing hierarchical Bayesian or Approximate Bayesian models on large spatial datasets
    • Record of academic publication

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