Data Science Interrn/ Graduate, NZ

Note: Partly is headquartered in Texas, with a Product and Engineering base in Christchurch, NZ and an early presence in London, UK. This role is to be based in the office at our Product and Engineering HQ in Christchurch, NZ.

Our story

Partly's mission is to connect the world's parts and we're doing that by building the first global platform for replacement parts, starting with auto parts. Our big vision is to accelerate the world toward a sustainable future where anyone can fix anything.

Founded by ex-Rocket Lab engineers, we utilise cutting-edge technology to solve challenging but exciting problems that make a huge impact in a $1.9 trillion industry. We've more than tripled our team over the last 12 months and expect to double in size again over the coming 12 months. We're a global team spanning both Europe and Australasia.

We provide a scalable digital infrastructure solution to some of the world's largest businesses and the most exciting startups. Partly's solutions are integrated across hundreds of companies globally, providing the backbone for cataloguing and managing parts online.

Our investors include Blackbird Ventures (Canva, CultureAmp etc.), Square Peg, Octopus Ventures, Icehouse, Peter Beck (Rocket Lab), Akshay Kothari (Notion Co-Founder) and Dylan Field (Figma Co-Founder).

We're continuing to build a world-class team and ensuring Partly is a place where people can do the best work of their lives. We're proud of the culture we've built at Partly, and our values are lived throughout every experience.

This role

As a Data Science Intern, you'll work alongside our Applied ML and DataQA teams to help make sense of the technical problems behind Partly's core vehicle and parts workflows. You'll be paired with experienced data scientists and engineers who will mentor you as you dig into messy, real-world data and turn it into clear, useful analysis.

The work sits between data science, domain analysis, and product. You'll help investigate why things like parts validation, parts ordering, automatching, and variant handling become difficult or unreliable, and you'll help quantify how often those problems happen and how much they matter. This is not dashboards-for-the-sake-of-dashboards and it is not pure model training. It's hands-on, investigative work on real problems, with real impact.

This is an internship for someone early in their journey who learns by doing. Expect to work directly with SQL and Python, ask good questions, build lightweight tools or analyses, and see your findings help the team decide what to fix next.

What will you do

Dig into real parts data. Help build a data-backed understanding of the problems that make parts validation, parts ordering, and automatching harder, with guidance from your mentor.

Help quantify problem types. Assist in breaking broad issues (for example "variants are messy") into specific, measurable categories, and counting how often each one shows up.

Measure where quality is lost. Use SQL, Python, sampling, and internal tools to help estimate accuracy, failure rates, and impact across makes, providers, and part groups.

Turn analysis into something useful. Help produce clear findings that Applied ML, Product, and DataQA can actually act on.

Build lightweight tooling. Pitch in on scripts, small dashboards, and review workflows that help others see and understand parts problems more clearly.

Learn how the team works. Partner with Applied ML, DataQA, and product folks, and pick up how a high-velocity, low-bureaucracy team operates.

Get the basics right. Reproducible analysis, clear notes, and thoughtful questions that make your work easy to build on.

Want to learn more about the problems we're solving and the culture we're building at Partly? Hear directly from our team here: https://shorturl.at/iAFUX

Your skills

Solid analytical fundamentals. You're studying or have recently studied data science, statistics, computer science, mathematics, engineering, or a related field, and you can reason carefully about data.

Working SQL and Python. Through coursework, projects, or prior experience, you can query data and write code to analyse it. You don't need to be an expert, but you should be comfortable getting your hands dirty.

Good problem decomposition. You enjoy taking a vague or messy question and breaking it into smaller, answerable pieces.

Curiosity about how things really work. You like understanding the "why" behind a problem, not just producing a chart, and you're comfortable with data that isn't clean or fully labelled.

Clear communicator. You can explain what you found and why it matters, ask for help when you need it, and take feedback well.

Bias for learning. You want to be stretched, you take ownership of your growth, and you're excited to work on hard, real-world problems rather than tidy textbook ones.

(Bonus) Any exposure to classification problems, data quality or QA work, entity matching, catalogue or automotive data, or working alongside ML or human-in-the-loop systems.

Please note: if you don't have all the skills or experience listed above but believe you could be outstanding in this role, please still consider applying. Many folks, especially those from underrepresented or marginalised groups, often count themselves out. Please allow us to learn more about you and why you're exceptional!