Job Description

LIC is a farmer-owned co-operative that’s been supporting herd improvement in New Zealand’s dairy sector for more than 115 years. We’re focused on providing superior genetics, reliable testing and diagnostics, integrated on-farm software, and great customer service, all designed to help farmers make smarter decisions every day.

As a proud generational co-operative, we’re here for farmers, their families, and rural communities, not just for today, but for the future. And none of it happens without our people. Whether on farm, in the lab, or behind the scenes, every one of us plays a part in helping New Zealand farmers succeed.

Project details:

Project Title

Enhancing Stochastic Simulation Models to Evaluate the Impact of Emerging Technologies on Genetic Gain in LIC's Dairy Breeding Programme

Project Scope:

This project explores how emerging reproductive technologies could shape the future of dairy cattle breeding in New Zealand. Using simulation modelling, genomic and pedigree data, the student will investigate the potential impacts of these technologies on genetic improvement, breeding strategies, and herd diversity, helping inform future industry decision-making.

Your contribution:

• Improve and enhance breeding simulation software used in dairy genetics research.
• Apply data science and computational modelling techniques to real-world industry challenges.
• Analyse genetic and breeding data to support evidence-based decision-making.
• Evaluate future breeding strategies through modelling and scenario analysis.
• Contribute to research that supports sustainable genetic improvement in New Zealand dairy herds.
• Work alongside leading scientists in quantitative genetics, genomics and animal breeding.
• Produce technical documentation and communicate research findings to support ongoing innovation.

Expected Outputs:

The project will deliver improved simulation tools, validated modelling outcomes, and new insights into future dairy breeding strategies. These outputs will help support long-term genetic improvement, sustainable breeding programme design, and the evaluation of emerging reproductive and genomic technologies within New Zealand's dairy industry.

Your Skills & Experience:

Essential

• Basic programming experience (Python preferred)
• Strong analytical and problem-solving skills
• Understanding of statistics and data analysis
• Ability to work independently and manage tasks
• Good written and verbal communication skills
• Attention to detail

Desirable

• Experience with simulation or modelling
• Familiarity with genetics, animal breeding, biology, or agricultural science
• Experience working with large datasets
• Experience with Linux and Bash scripting
• Familiarity with cloud computing environments, particularly AWS
• Knowledge of version control systems (e.g. Git)
• Experience with Python scientific libraries (e.g. NumPy, Pandas, SciPy)
• Interest in quantitative genetics, genomics, or dairy production systems

Application information:

To be eligible for these internships students must be:

• Legally permitted to work in New Zealand.
• Studying, or have studied, an undergraduate or postgraduate qualification at NZQA level 6-10 If study has been completed, the final semester should have ended within the last 12 months.
• Studying, or have studied, science, technology, engineering, design or business at a New Zealand tertiary education institute.
• Be able to work on-site at our Head Office in Newstead Hamilton for the duration of the internship

If you have worked for LIC before, you can still apply if:

• your job was part-time or temporary
• you only held one R&D Experience Grant internship with LIC.

Please attach an up-to-date CV and your academic transcript with your application.

The internship is a paid opportunity offering $29.90 per hour for a period of 10 weeks. You will be required to work a minimum of 30 hours per week, up to a maximum of 400 hours to be completed by 31 March 2027. Participation in the project will be based physically at LIC. At the conclusion of the internship, you will be required to deliver a presentation and submit a report.

Applications close: 20 September 2026 or earlier if the roles are filled