- Location
- Auckland, Auckland, New Zealand · published
- Type
- Full-time
- Department
- Education
- Source
- Workable
Description
Attention is the currency of entertainment. We turn it into decision intelligence.
Every search, stream, post and fan conversation is a signal of what people care about. Parrot Analytics measures and models global audience demand, helping leading studios, streaming platforms, broadcasters and investors make better decisions about what to create, acquire, value, market and fund.
We're looking for a Data Scientist to power the Data Intelligence behind our Insights and Partnerships teams. You'll tackle questions that go well beyond a SQL query: Who cares about an IP? Which audiences overlap? Why is sentiment changing? And what does it mean for a commercial decision?
From Auckland, your work can inform decisions involving some of the world's biggest franchises, talent and entertainment investments.
What you'll do
- Build Python-first intelligence workflows. Source, collect, clean and analyse digital signals from APIs, social platforms, online conversations and other structured or unstructured data. Use SQL and our data warehouse where useful, but go beyond routine reporting.
- Analyse attention, demand and audiences. Investigate momentum across titles, talent, franchises and markets; develop audience profiles, behavioural segments, overlap analysis and brand affinities.
- Map audience and piracy networks. Use graph and network analysis to uncover connections between content, communities, fandoms and consumption behaviour.
- Decode the conversation. Combine social listening and quantitative sentiment with LLM/RAG-based qualitative research to understand what audiences are saying, why it matters and which evidence supports it.
- Turn findings into decisions. Partner with Insights and Partnerships colleagues to interpret results, challenge assumptions, communicate uncertainty and tell clear, commercially relevant stories with data.
- Improve the engine. Refine methodologies, automate repeatable work, experiment with AI-powered approaches and make research workflows more accurate, scalable and reproducible.