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. This position is based in our Christchurch office. 🚀 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 towards a sustainable future where anyone can fix anything. Founded by ex-Rocket Lab engineers, we utilize 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 cataloging 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. 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 🖍️ 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 query data and write code to analyze 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 labeled. 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, catalog data, 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 people count themselves out. We'd love the chance to learn more about you and why you're exceptional. 🎡Our
Benefits Healthy, Catered Lunches - Enjoy fresh, healthy lunches every workday in our Auckland, Christchurch, London and Austin offices. With no meal prep needed, you can eat, connect, and refuel with your team. (And yes, snacks and drinks are always on hand.) Healthy Body, Healthy Mind - We care about performing at our peak. Every team member gets a $1,500 annual wellness allowance (or local equivalent) on a Partly-branded card. Use it on things such gym memberships, rock climbing, physio, massage, GP visits, prescriptions; anything that you or your family, need! Family Comes First - Primary caregivers receive 3 months of fully paid parental leave, plus a flexible return-to-work (four days on full pay for your first three months back). Getting Here Is On Us - If you commute to a Partly office or co-working space, choose from a paid 24/7 car park or commute allowance. One less thing to think about! Workspaces That Inspire - Our brand new, architecturally designed offices are built for collaboration and creativity, with great coffee, social spaces, and some of the best cafes a few steps away. Office-First with Flexibility - In cities where we have an office (Christchurch, Auckland, London, San Francisco), we default there every day. This let's us move faster, make better decisions and build strong relationships. We also operate with a very high trust environment, so you can manage your time around your life, and flex your schedule to get your best work done. We Celebrate Together - From weekly happy hours and monthly lunches to quarterly season openers and an annual global offsite, we make time to connect, celebrate, and have fun as one team. 🛬 Relocation If you are relocating from overseas or domestically to Partly HQ, we offer a generous relocation allowance to support your move
About Partly
Partly develops digital infrastructure for the automotive parts industry, providing software, APIs, and AI models that support parts identification, procurement, and supply chain operations. Its platform standardizes communication and data exchange among participants in the parts ecosystem, helping businesses integrate parts information into their existing systems and workflows. The company offers both developer tools and pre-built applications that can be embedded into enterprise platforms or deployed as standalone solutions. Its products are used by automotive groups, repair businesses, dismantlers, and other organizations to manage inventory, pricing, ordering, and cataloging processes. Partly also develops industry-specific AI models designed to improve the accuracy and efficiency of parts-related operations.
