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Data Scientist Jobs in New Zealand
62 results
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Momentum Consulting Group Limited
ML Engineer / Senior ML Engineer
Hybrid • Full Time$140 - $200 k/yr
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Icehouse Ventures
Hiring for TracksuitPrincipal Measurement Scientist
Hybrid • Full TimeStarting at $176 k/yr
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Ministry for Primary Industries
Senior Data Analyst
On Site • Full Time$88.75 - $120.37 k/yr
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Accenture New Zealand
AI/ML Computational Science Specialist
On Site • Full Time$100 - $120 k/yr
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VRChat
Senior Data Scientist
- Posted 4d ago
- Remote
- Full Time
Job Description
Join the VRChat Team!
VRChat offers a first-of-its-kind, game-changing platform that provides an endless collection of social VR experiences and gives the power of creation to its robust community. With over 250,000 worlds and growing, VRChat’s vision is to allow users to bring their imaginations to life and help shape the metaverse anywhere in the world on any device. VRChat has raised $100M to date with the support of investors Makers Fund, Anthos Capital and HTC. VRChat has grown with talent from: Netflix, Twitter, Meta, Microsoft, Roblox, Google, Amazon, Unity, Spotify, Discord, Uber, eBay, Robinhood, Twitch, Zynga and TikTok.
Come and join the mission!
Job Overview
As a Senior Data Scientist on VRChat’s Data Team, you will play a key role in driving strategic product decisions and advancing our product roadmap across growth, user experience, creator, and economy verticals. The ideal candidate will have a background in a technical field, will have experience working with large production data sets, and will have some experience delivering data products on a fast-paced team.
This position will be full-time and remote.
What You’ll Do
- Partner with cross-functional teams to define high-impact product questions, success metrics, and decision frameworks.
- Analyze user behavior, creator activity, content ecosystems, retention, growth, monetization, and social dynamics to identify opportunities and risks.
- Design and analyze experiments, including A/B tests and other causal measurement approaches, to evaluate product launches, campaigns, and strategic initiatives.
- Define and improve core product metrics, dashboards, diagnostic views, and recurring reporting that help teams understand what is changing and why.
- Build durable analytical assets such as reusable notebooks, data marts, metric definitions, self-serve dashboards, and analysis templates.
- Use AI-assisted and agentic workflows to accelerate analysis, automate repetitive investigation, improve documentation, and build lightweight internal tools for common analytical questions.
- Collaborate with Data Engineering and product teams to improve data quality, instrumentation, modeling, and source-of-truth definitions.
- Communicate findings clearly to technical and non-technical audiences, including senior stakeholders, and influence product decisions through evidence and judgment.
What We’re Looking For
- 5+ years of experience in data science, product analytics, data analytics, quantitative analysis, or a related analytical role.
- Expert SQL skills and strong proficiency with Python or R for analysis, experimentation, modeling, automation, and reproducible workflows.
- Strong product sense, with experience translating ambiguous product or business problems into structured analyses, metrics, experiments, and recommendations.
- Experience designing, analyzing, and interpreting experiments, including A/B tests, guardrail metrics, and common statistical pitfalls.
- Experience building dashboards, reporting systems, reusable analyses, or data products that help stakeholders self-serve and make better decisions.
- Familiarity with AI-assisted analytical workflows, such as using LLMs or agents for code generation, exploratory analysis, documentation, QA, summarization, or workflow automation.
- Strong judgment around data quality, statistical validity, reproducibility, privacy, and the limitations of AI-generated analysis.
- Excellent communication and stakeholder management skills, including the ability to explain complex findings clearly and influence senior audiences.
- Bachelor’s or Master’s degree in a technical, quantitative, or data-related field, or equivalent practical experience.
Bonus Points
- Experience at a social, gaming, creator economy, marketplace, consumer subscription, or UGC platform company.
- Experience with social graphs, creator ecosystems, virtual economies, recommendations, lifecycle analytics, retention, churn, pricing, trust & safety, or community health metrics.
- Experience building AI-powered internal tools, analytical agents, natural-language data interfaces, automated reporting, or self-service insight systems.
- Experience with tools such as dbt, Snowflake, BigQuery, Tableau, Amplitude, Hex, Mode, Jupyter, Streamlit, Airflow, Dagster, or similar platforms.
- Experience mentoring other data scientists, analysts, or cross-functional partners.
Benefits
- Work from anywhere! VRChat is a 100% remote company
- Health Benefits
- 401K for US & RRSP for Canadian Employees
- Stock Options
- Generous paid holiday schedule
- Unlimited/Flexible vacation time
- Paid parental leave
VRChat is an equal-opportunity employer, and we welcome applicants from all backgrounds. VRChat fosters a diverse, creative, and collaborative environment where anyone can contribute to any of the ongoing projects or direction of the roadmap at any time. If you’re a passionate team player who wants to have an impact on a dynamic team, we’d love to hear from you!
All job offers are subject to satisfactory referencing and background checks.
Desired Hard Skills
- Cloud Infrastructure
- Data Warehousing
- ETL Pipelines
- Python Scripting
- SQL Programming
Desired Soft Skills
- Adaptability
- Collaboration
- Communication
- Problem Solving
- Strategic Thinking
Job Schedule
- Shift Work
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Data Scientist Jobs
Data scientist jobs in New Zealand are turning up across global consultancies, Kiwi product companies, fitness technology, media, infrastructure, and advertising platforms. Employers are using data and AI to solve real business problems, from optimising digital products and fraud detection to improving customer experiences and operational efficiency. Many roles sit inside dedicated Data, Analytics and AI teams, where data scientists work alongside engineers, analysts, product managers, and business leaders to turn raw data into useful insights and intelligent systems. As more organisations invest in cloud platforms, machine learning and generative AI, opportunities are growing for people who can translate complex data into measurable impact.
Day-to-day work
Day to day, data scientists in these roles work across the full lifecycle of data and machine learning projects. They explore and prepare data, build and test models, and help deploy them into production through close work with data engineers and MLOps specialists. A typical week might include designing experiments, improving recommendation or optimisation models, tuning generative AI or language models, and monitoring how those models perform in real products. Many roles combine hands-on coding in Python or SQL with using cloud tools, data warehouses and visualisation platforms to surface insights. There is also a strong focus on documentation, governance and collaboration, so models are explainable, secure, and trusted by stakeholders.
Career progression
Careers as a data scientist often progress into more senior or specialised paths as experience grows. Some move into senior or lead data scientist roles, owning key products or domains and guiding technical direction. Others step into adjacent roles such as machine learning engineer, analytics lead, or data engineer, focusing more on scalable systems, experimentation platforms or data infrastructure. There are also opportunities to move into leadership positions such as Senior Analytics Manager or Head of AI and ML, where the focus shifts to strategy, team building, and setting an organisation wide roadmap for AI and data. In some companies, experienced data scientists also move into product or commercial roles, helping shape how data driven products are taken to market.
Skills employers look for
Employers hiring into these roles look for a blend of strong technical skills and practical problem solving. Common requirements include proficiency with Python, SQL and modern machine learning frameworks, as well as experience working with cloud platforms and data warehousing tools. Many roles value familiarity with production environments, from MLOps and CI/CD through to monitoring and retraining models once they go live. Just as important are soft skills such as clear communication, stakeholder engagement, and the ability to explain complex ideas in simple language for non technical teams. Curiosity, a willingness to learn new tools, and a responsible approach to privacy, security and ethical AI are also heavily emphasised.
Find your next role
If you are ready to use data, analytics and AI to solve real problems, there are plenty of data scientist jobs to explore. Browse current listings on ZEIL to compare industries, tech stacks and team structures, and find a role that matches your skills, values and ambition.