Fisheries Scientist—Statistical Modelling

January 24, 2018

We are looking for someone with excellent statistical modelling skills to work on and lead projects related to fisheries and their environmental impacts. Sustainable management of fisheries depends on informed decision making. You will be providing technical advice to ensure that fisheries are well-run, sustainable, and of low impact. You will be joining our team in Wellington, New Zealand.

The role

We seek a fisheries or environmental scientist who is comfortable developing Bayesian statistical models and using them to analyse data. Experience with complex models, such as fisheries stock-assessment models, or spatio-temporal models will be required. Our Linux-based toolchain revolves around open-source software such as R, Python, PostgresSQL, and we would prefer a person who has actively coded using these tools. Experience with statistical modelling platforms such as Stan, JAGS, or Template Model Builder,
would be ideal.

A Ph.D. in a relevant field and postdoctoral experience are desirable. At a minimum, candidates must have an M.Sc. in a relevant field, such as fisheries, quantitative marine ecology, seabird or marine mammal bycatch, or applied statistics. At M.Sc. level, demonstration of relevant research experience post-M.Sc. will be required.

This position requires communication with a wide range of stakeholders, including other researchers, non-government organisations, government agencies, and industry representatives. We are looking for a thoughtful and considerate communicator, who is able to make their work relevant to a broad range of people.

Through this position, the successful person will develop skills and expertise that are applicable to a range of science environments, including academic, government or private sectors. We seek to provide an environment to support this professional development. There is room to contribute to existing projects and proposals, lead projects, develop new streams of research, or seek funding to continue existing streams of work.

The role is offered as a full-time permanent position, with the salary depending on experience, and five weeks annual leave. Usual terms and conditions under New Zealand labour law apply. We anticipate that the role will be based in Wellington, and applicants must be eligible to work in New Zealand. We can offer flexible working arrangements, if required, that may suit people with young families. For example, some of our staff live semi-rurally and work partly from home.

Working at Dragonfly

Dragonfly Data Science ( are a small consultancy. We are a collaborative team, taking pride in our ability to work together, and draw on substantial expertise in a range of domains. While individuals are responsible for particular projects, outputs are products of our teamwork. This role will suit a curious, open-minded and optimistic person, who is willing to take advantage of new opportunities as they arise. We strongly encourage applications from Māori, from women, or from people who will add to the diversity of our team.

Dragonfly Data Science works with a broad range of clients, with new opportunities presenting regularly in fields ranging from fisheries, to genomics, language modelling, and reproducible research. We are ambitious, and taking advantage of these opportunities means we need someone to help with aspects of our fisheries work.

Some relevant fisheries projects that we are currently working on include: risk assessment of the impact of fishing on New Zealand seabird and marine mammal populations; research on the impact of climate change on marine ecosystems; developing spatial models for New Zealand pāua fisheries; and assessment of the impact of fisheries on Pacific whale-sharks.

While most of our research is applied, we support fundamental science projects that can complement and inform our other work. The successful candidate will be encouraged to publish their work, as well as build and maintain relationships with new and previous colleagues in New Zealand and overseas.


Please send a cover letter (a maximum of two pages), a curriculum vitae including the names and contact details of three referees, and examples of recent research outputs to Louise Alliston ( Applications close at the end of Friday, 23 February 2018.