Data Science Intern/Graduate

About Partly

Partly, with its headquarters in Austin, Texas, and offices in London, Auckland, and Christchurch, New Zealand, is revolutionizing the global repair industry by building AI infrastructure to understand vehicle damage and required parts. Their pioneering AI model, Interpreter, serves thousands of businesses worldwide in the $2 trillion automotive market. Founded by former Rocket Lab engineers, Partly has rapidly grown and secured $50 million in Series B funding from major investors.

Role Overview

This internship position is tailored for early-career individuals passionate about data science applied to real-world automotive parts workflows. Interns collaborate with Applied ML and DataQA teams, tackling complex problems related to parts validation, ordering, automatching, and variant management. The work combines data science, domain expertise, and product insights, emphasizing hands-on investigation rather than theoretical or pure modeling tasks.

Key Responsibilities

• Analyze authentic parts datasets to identify and understand challenges in parts workflows under mentor guidance.
• Deconstruct broad issues into quantifiable, specific categories and determine their frequency.
• Evaluate data quality by estimating accuracy, failure rates, and impacts using Python, SQL, sampling, and internal tools.
• Transform analytical outcomes into actionable insights for Applied ML, Product, and DataQA teams.
• Develop lightweight tools such as scripts and mini dashboards to enhance visibility into parts problems.
• Collaborate cross-functionally with Applied ML, DataQA, and Product teams within a fast-paced environment.
• Ensure reproducibility of analyses, maintain clear documentation, and engage proactively through thoughtful questioning.

Candidate Skills and Qualifications

• Currently studying or recently graduated in fields like data science, statistics, computer science, mathematics, engineering, or similar.
• Proficient with SQL and Python from academic projects, coursework, or prior experience, capable of working with raw data.
• Adept at problem decomposition, able to break down ambiguous questions into manageable components.
• Curious about underlying mechanisms, comfortable handling incomplete or unstructured datasets.
• Strong communication skills to clearly articulate findings, request guidance, and receive feedback constructively.
• Motivated learner eager to grow through challenging, practical problems beyond textbook examples.
• Additional advantage: familiarity with classification tasks, data quality assurance, entity matching, catalog or automotive data, or integration with ML/human-in-the-loop frameworks.
• Applicants lacking all specified skills but confident in their potential are encouraged to apply.

Benefits

• Daily nutritious catered lunches available in offices worldwide, including Auckland, Christchurch, London, and Austin.
• Annual wellness allowance equivalent to $1,500 for health-related expenses like gym, physiotherapy, prescriptions, and more.
• Paid parental leave for primary caregivers (three months) with flexible part-time return options.
• Commuter perks include paid 24/7 parking or allowances for office-bound employees.
• Modern, architecturally designed workspaces conducive to collaboration and creativity, situated near top cafes.
• Office-first culture with flexibility to optimize work-life balance in cities with offices.
• Regular social events including weekly happy hours, monthly lunches, quarterly team gatherings, and annual global meetings.

Additional Information

Partly supports relocation with a generous allowance and covers travel and accommodation for quarterly team gatherings. Onboarding connects you with the nearest office.