Water Quality Data Analyst / Modeller

Hamilton or Christchurch, New Zealand
Position Type: 
Experience Level: 
Not Specified
Degree Required: 


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Permanent, full-time

NIWA is a dynamic research organisation whose purpose is to enhance the economic value and sustainable management of New Zealand's aquatic resources and environments, to provide understanding of climate and the atmosphere and increase resilience to weather and climate hazards to improve safety and wellbeing of New Zealanders. NIWA is New Zealand's largest and pre-eminent provider of climate, freshwater and marine science.

We are seeking an adaptable biophysical scientist, who has experience with application of data analysis and modelling to cover a wide range of NIWA's water quality research and consultancy projects. You will be compiling, conditioning, storing and modelling environmental data sets, which are often large, for which proficiency in R is essential; developing analytical programmes, packages and models; developing and applying novel data analysis techniques for water quality data; conducting statistical-based risk assessments (e.g., quantitative microbiological risk assessments, QMRA); assisting with design of water quality surveys and experiments; providing mathematical and statistical mentoring and coaching on statistical methods

There is an expectation that you will also be involved in, and at times, taking responsibility for developing research programmes and undertaking consultancy projects

You will need to have:

  • A PhD in environmental statistics, environmental data science or any of the following (with strong data analysis/modelling skills): aquatic chemistry, aquatic ecology, aquatic pollution mitigation, aquatic microbiology, catchment processes or environmental engineering
  • Several years' experience in carrying out scientific research and consulting and applying a range of modern statistical approaches.
  • Proficiency in the use of R and a reasonable knowledge of water quality and water resources concepts (essential).