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Computational Ecologist Job at NOAA in Silver Spring, MD (USA)

The NOAA National Centers for Coastal Ocean Science is hiring a Computational Ecologist, a statistical/computational ecologist with experience fitting advanced spatial models to marine wildlife survey data (e.g., seabirds and marine mammal transects, fisheries trawl surveys) in R and other statistical languages. This is a full-time, long-term stable contract position. We are looking for an expert R programmer with experience in spatial modeling, especially of marine wildlife survey transect data (e.g. seabirds, marine mammals). Please note that this is a contract position, so rather than applying directly to NOAA the link below directs you to the contracting company
(CSS-Dynamac, Inc.). We are looking to hire someone immediately.

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*Computational Ecologist*


Contract position with *NOAA's National Ocean Service, National Centers for Coastal Ocean Science, Biogeography Branch* (Contract Company: http://www.css-dynamac.com/)

*Apply for this job online at :
https://jobs-consolidatedsafety.icims.com/jobs/1486/job
*
*Responsibilities*:

A person with experience or academic training in quantitative ecology, advanced statistical modeling, computational analysis, and scientific programming in R and Matlab; who also has demonstrated interest and experience in advanced spatial analysis, is being sought for a full-time contract position with the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Coastal Ocean Science
(NCCOS). NCCOS' Biogeography Branch conducts ecological and oceanographic studies to map, characterize, assess, and model the spatial distributions and movements of marine organisms across habitats throughout the United States and Island Territories. We are seeking an individual with a broad suite of quantitative, statistical, and computational skills. A strong background in statistical modeling of
spatial ecological data with some experience in marine sciences is preferred. The successful candidate will join an experienced scientific team at the forefront of marine ecological predictive analytics. The initial assignment for this position will involve developing, implementing, and running machine-learning models for predictive
spatio-temporal modeling of marine bird and groundfish distributions to support marine planning processes. Additional potential projects include predictive modeling of deep sea corals, marine mammals, sea turtles, marine fish, fishing fleets, and marine ecosystem processes.

*Core responsibilities*

1. Provide statistical, computational, and analytical support to projects that use predictive statistical models, in conjunction with large wildlife survey and oceanographic databases, to provide 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


2. 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


3. 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

*Qualifications*:

*Essential*

* 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*

* 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

Apply for this job online at
https://jobs-consolidatedsafety.icims.com/jobs/1486/job

To discuss the position or for more information contact:
 

Dr. Brian Kinlan
Marine Spatial Ecologist
NOAA NOS National Centers for Coastal Ocean Science
Brian.Kinlan@NOAA.gov

 



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