Data Science Intern/Graduate, NZ

About Partly

Partly is a technology company building a global platform for replacement parts, beginning with the auto parts market. Its long-term goal is to help create a more sustainable future where fixing things is easier for everyone.

The company was started by former Rocket Lab engineers and applies advanced technology to tackle difficult, high-impact problems in a $1.9 trillion industry. The team has grown rapidly over the last year and continues to expand across Europe and Australasia. Partly works with a wide range of customers, from major enterprises to fast-moving startups, and its infrastructure supports parts cataloguing and management across hundreds of businesses worldwide.

Partly is backed by investors such as Blackbird Ventures, Square Peg, Octopus Ventures, Icehouse, Peter Beck, Akshay Kothari, and Dylan Field. The company aims to build an environment where people can do the strongest work of their careers, with values reflected in the way the team operates every day.

Role overview

This internship sits with the Applied ML and DataQA teams and is based in the Product and Engineering office in Christchurch, New Zealand. The role focuses on understanding technical issues within core vehicle and parts workflows, especially around parts validation, parts ordering, automatching, and variant handling.

You will work closely with experienced data scientists and engineers who will guide you as you investigate messy real-world data, identify patterns, and turn your findings into practical analysis that supports product and technical decisions. This is a hands-on role for someone early in their data science journey who enjoys learning by doing.

What you will do

• Explore real parts data and help build a clearer, data-led view of the issues affecting parts validation, ordering, and automatching.
• Break broad problems into measurable sub-issues and estimate how often each type occurs.
• Use SQL, Python, sampling, and internal tools to assess accuracy, failure rates, and business impact across makes, providers, and part groups.
• Convert analysis into outputs that Applied ML, Product, and DataQA teams can use in decision-making.
• Contribute to lightweight scripts, small dashboards, and review processes that improve visibility into parts-related issues.
• Work with Applied ML, DataQA, and product stakeholders in a fast-moving, low-process environment.
• Keep analysis reproducible, document your work clearly, and ask thoughtful questions so others can build on your output.

What they are looking for

• Strong analytical foundations, ideally from study or recent study in data science, statistics, computer science, mathematics, engineering, or a related discipline.
• Practical SQL and Python ability gained through coursework, projects, or prior experience.
• Comfort with breaking down ambiguous or messy problems into smaller, solvable parts.
• Genuine curiosity about root causes and how systems work, including working with incomplete or imperfect data.
• Ability to communicate findings clearly, explain why they matter, and respond well to feedback.
• A strong desire to learn, take ownership, and work on challenging real-world problems.
• Bonus exposure to classification, data quality or QA work, entity matching, catalogue or automotive data, or human-in-the-loop/ML workflows.

Application encouragement

Candidates who do not meet every listed requirement are still encouraged to apply if they believe they could excel in the role. The company welcomes applicants from underrepresented and marginalised backgrounds and wants to hear from people who may otherwise hesitate to put themselves forward.

Location

This position is office-based at Partly’s Product and Engineering headquarters in Christchurch, Canterbury, New Zealand.