Christchurch, Canterbury, New Zealand · On-site
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 Christchurc…
Skills: SQL, Python, Data Analysis, Problem Decomposition, Analytical Reasoning
Christchurch, Canterbury, New Zealand · Hybrid
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Skills: Machine Learning, Algorithms, Data Structures, Modelling, Ranking
Christchurch, Canterbury, New Zealand · Hybrid
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Christchurch, Canterbury, New Zealand · On-site
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Christchurch, Canterbury, New Zealand · On-site
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Christchurch, Canterbury, New Zealand · Hybrid
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Christchurch, Canterbury, New Zealand · On-site
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Christchurch, Canterbury, New Zealand · Hybrid
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Christchurch, Canterbury, New Zealand · Hybrid
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Christchurch, Canterbury, New Zealand · On-site
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Christchurch, Canterbury, New Zealand · Hybrid
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Company Description Join us. But be you. At Deloitte, you’ll do more than just join something, you’ll add something. You’ve found a place that values your curiosity, passion, and desire to work alongside brilliant people…
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Christchurch, Canterbury, New Zealand · Hybrid
Company Description Join us. But be you. At Deloitte, you’ll do more than just join something, you’ll add something. You’ve found a place that values your curiosity, passion, and desire to work alongside brilliant people…
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Christchurch, Canterbury, New Zealand · Hybrid
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Christchurch, Canterbury, New Zealand · Hybrid
Company Description Join us. But be you. At Deloitte, you’ll do more than just join something, you’ll add something. You’ve found a place that values your curiosity, passion, and desire to work alongside brilliant people…
Skills: IT Controls Assurance, IT Audit, General IT Controls, Automated Controls, IT Risk Management
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The role involves analyzing real-world parts data to identify and quantify technical problems in vehicle and parts workflows. You will build lightweight tools and produce actionable findings to support Applied ML, Product, and DataQA teams.
Candidates should be current students or recent graduates in data science, statistics, computer science, or a related field with working knowledge of SQL and Python. Strong analytical fundamentals and a curiosity for solving messy, real-world data problems are essential.
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.
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.
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.
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
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!
Partly's mission is to connect the world's parts. We're a team of automotive veterans and world-class engineers, with backgrounds at Rocket Lab, Microsoft, F1, Apple, and Intel. As a result of our team's collective and unusually diverse experience, we have the perspective to understand what needs fixing and the expertise to actually fix it. We're building the first foundational AI model for the auto parts industry. Our technology delivers efficiencies across the entire collision parts supply chain - from procurement to order management. Partly is independent from any automotive body and backed by industry-leading investors including Octopus Ventures, Blackbird, Squarepeg, I2BF, Ten13, Shasta Ventures, Icehouse Ventures, Peter Beck (Rocket Lab), Randy Reddig (Square), Dylan Field (Figma), and Akshay Kothari (Notion). Partly is UK-based, expanding into the US, with our core engineering team based in Christchurch.
Offices: 200 Armagh St, Upstairs, Christchurch, Canterbury 8011, NZ · Fora Central Street, 71 Central St, London EC1V 8AB, United Kingdom, London, London EC1V 8AB, GB · 149 New Montgomery St, San Francisco, California 94105, US
There are currently 20 open data & analytics positions in Christchurch, New Zealand listed on Clera. New openings are added daily as companies post roles.
Companies currently hiring include Deloitte, Foodstuffs South Island, Partly, Datacom, Appetise.com, among others. Browse the listings above to see every active employer.
Yes — 15 of the 20 open data & analytics positions offer remote or hybrid work (1 remote, 14 hybrid).
Each listing links directly to the employer's application page. Apply early — fresh listings get the most recruiter attention in the first two weeks.
