Project Descriptions

Details of our internship projects for our 2018-19 summer programme are outlined below. Please be aware that this is not a comprehensive list of all of the projects we have on offer. There will be more added and some that will not be listed on here which could be located anywhere in New Zealand. Please make sure to indicate where you are able to be located for your internship in your application form.

  • Machine learning for te reo Māori (Te Hiku Media, Wellington or Auckland, location negotiable):

Te Hiku Media’s Kōrero Māori project aims to develop natural language processing tools for te reo Māori. Te Hiku have over 370 hours of spoken corpus and have built the first Māori speech to text engine available at As an intern, you will have the opportunity to study the corpus and apply it to other applications, improve current machine learning (ML) models by investigating the parameter space of the frameworks they use, and test new ideas focused around building ML tools from a te reo Māori perspective (e.g. many frameworks are built for English which isn’t very effective for phonetic languages). Your work will help create digital te reo Māori tools such as Māori speaking personal assistants, language learning apps that use ML to provide immediate feedback to learners, and transcription tools that will enhance access to thousands of hours of native speaker archives.

  • Auckland 2050 – He waka eke noa (Ngāti Whātua Orākei, Auckland):

Te Pūnaha Matatini researchers and students, in conjunction with Ngāti Whātua Ōrākei will collate qualitative data from existing sources that express the values, aspirations, and Mātauranga Māori of the hapū, specifically related to traditional management of resources, knowledge, and health. The team will analyse the qualitative data using a combination of discourse analysis and thematic analysis techniques, both digital and traditional, creating a rich resource of Ngāti Whātua Ōrākei knowledge, and enabling Ngāti Whātua Ōrākei to clearly define strategic intent for future policy development and technological innovation within whānau voices and stories.

  • Social networks in pre-European Aotearoa (Te Pūnaha Matatini, Auckland):

Thegn Ladefoged (anthropology) and Dion O’Neale (physics) are investigators in Te Pūnaha Matatini, collaborating on a Marsden project that uses information from obsidian artefacts to infer social networks in pre-European Aotearoa.

  • Social network analysis of the biosecurity tourism landscape in New Zealand (AgResearch, Hamilton):

Working with a social scientist to undertake a social network analysis of information-seeking among tourism providers regarding biosecurity in New Zealand. This will involve conducting an online survey, sorting and analysing the data, and generating network maps. A final report will be required at the conclusion of the internship, reporting the results of the analysis, co-written by the intern and key researcher on the project.

  • Self-service data and analytics portal (ACC, Wellington): 

Developing and applying new approaches to data presentation, visualisation and analysis as part of the ACC’s transition to a new data and analytical platform. Applying a predefined methodology to understand user requirements, make data available within the new platform, produce reports and develop of new ways to access and use data.

  • Reforming New Zealand’s justice sector (Ministry of Justice, Wellington):

Developing evolving datasets and, with guidance, extracting a number of discrete analyses for Sector Group, which is responsible for leading the reform programme for the Justice Sector. Using data to generate insight and produce two-page fact-sheets which support the evidence base for reform of prison legislation, policy and practice.

  • Using data insights to deliver better justice services (Ministry of Justice, Wellington):

Undertaking a range of analyses to understand the root cause of issues and identify opportunities, helping the Ministry of Justice deliver critical services effectively to meet desired outcomes. Using data to uncover insights, and produce engaging outputs which support the evidence base leading to service design improvements.

  • Using data insights to deliver operational improvement for New Zealand’s justice sector (Ministry of Justice, Wellington):

Contributing to data analysis, performance reporting, and “what-if” modelling across the Ministry of Justice.  The data-driven insights generated from this work will ensure the Ministry’s operational leaders have a clear view of external trends and operational issues, and allow them to focus their front-line improvement efforts on operational areas that will make the biggest difference to New Zealanders.

  • International Visitor Survey Analysis (MBIE, Wellington):

Consulting with subject matter experts to identify key areas of interest, working with the research team to identify appropriate methods, undertaking data analysis and producing a report.

  • Develop an environmental sustainability dashboard (MBIE, Wellington):

Working with other agencies such as DOC and MfE, undertaking an environmental scan of available data on visitor use and sustainability of environmental assets. Creating a dashboard, and associated documentation including the identification of data gaps.

  • Building the evidence base about family and sexual violence: Literature review and analysis (MSD, Wellington):

MSD is building a durable research and evaluation infrastructure for family and sexual violence. Part of this work involves collating and synthesising existing evidence. The intern will take a lead role in completing a literature review and analysis on one or more topics, as well as supporting the team on related work.

  • Building on a prototype app for MSD’s employment assistance programmes (MSD, Wellington):

MSD has developed a prototype app that presents contextual information and outcome results related to its employment assistance programmes. The intern will undertake a project that will contribute to the new app by designing, producing and testing options for new data visualisations.

  • Building a prototype app that would aggregate information about MSD’s income support payments (MSD, Wellington):

MSD has work underway to better communicate and store the information it holds about its income support system. The intern will undertake a project that will contribute to the new app that brings together information about MSD’s income support payments.

  • Review of potential benefits of linking administrative and survey data for research and policy practice (MSD, Wellington):

This project involves drafting a critical review of relevant research using linked survey and administrative data, and identifying case studies relevant to social sector agencies that demonstrate the diverse ways in which linked survey and administrative data can inform policy and service delivery. The intern will summarise and present their findings to MSD managers and the Research and Evaluation team.

  • Data science in social services (MSD, Wellington):

MSD’s Client and Business Intelligence Unit  is responsible for designing innovative solutions to complex analytical problems, using and supporting the latest tools and methods required to undertake analytics, then translating this into practical real-world solutions for frontline staff and senior management. Intern projects are likely to cover topics across predictive modeling, natural language processing, and cloud thinking and capability.

  • Automation of sampling error production using R (Stats NZ, Wellington)

Sampling errors are an important quality indicator in sample survey results. Stats NZ uses them to decide when results are significant enough to comment on in media releases of data, and they are also available to help data users understand the confidence to put in survey estimates. Calculating and tabulating these can be a large task, especially when additional tables or variables are required, or a new survey is run. We have made a start on automating this process, and are looking for a suitable intern to complete this work. The required product will be an R package that provides functions to calculate sampling error as well as customise the outputs and create and format reports. It needs to work with different data sources (different surveys) and be well modularised so it can be easily modified to meet future requirements.