Data Scientist- Decision Support

Parrot Analytics is the global authority on media and entertainment intelligence, providing the strategic decision support that the world’s leading studios, producers, streamers, investors, and government bodies rely on to de-risk content investment and maximize returns. Trusted across the full media economy — from studios and streaming platforms to film funds, sports leagues, and government bodies — Parrot Analytics informs capital allocation, acquisitions, programming strategy, and IP valuation at the highest levels of the industry. By measuring the demand and preferences of more than 2 billion audiences worldwide, Parrot Analytics’ AI platform quantifies the value of content, talent, franchises, and sports rights — enabling partners to forecast revenue, assess risk, optimize portfolio strategy, and drive more predictable success.

About the RoleWe are seeking a curious and growth-oriented recent postgraduate to join our data science team as a Data Scientist. This role is ideal for someone who utilises AI to scale your impact. You instinctively reach for agentic workflows, LLM-assisted coding, and AI-enabled automation into how you think, analyze and solve problems.  You bring strong statistical foundations and solid analytical capability, but what sets you apart is how you work: embedding AI into every layer of your process, from exploratory analysis to communication to problem structuring. You will work on large, complex datasets to generate insights, support decision-making, and help improve how data is structured, accessed, and used across the organization. You will partner closely with data scientists, platform and data engineering teams, product managers, designers, and business stakeholders to solve ambiguous problems.  This role offers the opportunity to build real technical depth while contributing to meaningful product, platform, and business outcomes.

What You’ll Champion• Unlock Insights from Complex Data- Identify patterns, anomalies, and opportunities across large, high-volume datasets to generate actionable insights that drive product, platform, and business decisions. • Apply Advanced Analytical Thinking- Use statistical methods such as hypothesis testing, regression, forecasting, segmentation, experimentation, causal inference, and anomaly detection to solve business problems. • Strengthen Data Foundations- Partner with platform and data engineering teams to investigate data at scale, validate assumptions, and improve data quality, metric definitions, and analytical datasets. • Translate Ambiguity into Clarity- Turn open-ended business and platform questions into structured analytical approaches, practical recommendations, and scalable decision-support outputs. • Build Scalable Analytical Solutions- Develop repeatable analyses, dashboards, prototypes, and workflows that empower both technical and non-technical stakeholders to make better decisions. • Leverage Modern Data Tooling- Use SQL and Python and/or R to efficiently query, prepare, analyze, and model data within cloud and distributed environments. • Embed AI into Workflows- Apply LLMs and GenAI tools to accelerate coding, exploratory analysis, documentation, prototyping, and insight generation. • Advance Data & AI Capabilities- Evaluate and apply analytical, machine learning, and AI-driven approaches where they improve speed, quality, or decision-making impact. • Communicate Insights with Impact- Present findings through clear storytelling, visualisations, reports, and presentations tailored to diverse audiences. • Enable Reproducibility and Scale- Document methods, assumptions, code, and outputs to ensure transparency, reproducibility, and reuse across the team.