Data Science Intern/Graduate, NZ at Partly in Christchurch

Job description

Note: Partly is headquartered in Austin, TX with offices in London, UK, Christchurch, NZ and Auckland, NZ. Wherever you're based, we'll connect you with your nearest office for onboarding, and fly you to join the full team for our quarterly "Season Openers" (we cover travel and accommodation). If you're relocating to join us, we can also assist with relocation costs. 🚀 Our Story Partly is building the infrastructure for anyone to repair anything. We believe everything built should be repairable, and we're building frontier AI to put repair knowledge to work, starting with the $2tn automotive market. Our model, Interpreter, is the first AI purpose-built to understand repair. Trained on 1M+ car models and 1B+ parts, it reads a repair job the way an estimator does, resolves damage into the exact purchasable part, and shows its reasoning with a confidence score. On real production jobs, an estimator working with Interpreter reaches 98.8% parts-list accuracy, against 92.1% with today's tools. It powers PartlyRepair, used by collision shops, workshops and suppliers to take the admin off the job; PartlyLabs, the agentic infrastructure enterprises use to deploy Interpreter across their own workflows; and a developer API. Founded by ex-Rocket Lab engineers, we've raised a $50m Series B led by DST Global (Anthropic, Airbnb, Meta, TikTok, Spotify), with Blackbird Ventures, WNDR, Activant Capital, Icehouse Ventures, Square Peg, Airtree and Ecliptic Venture Capital. Hear it from the team: Why Partly exists and AI at Partly. 🖍️ 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 teams, and learn how a high-velocity, low-bureaucracy team operates. • Get the basics right. Produce reproducible analyses, write clear notes, and ask thoughtful questions that make your work easy to build on. 🥷 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