Data Analyst

Andersen is hiring a Data Analyst for a project developing a large-scale media intelligence platform that delivers real-time audience insights for the media and advertising industries.

The platform analyses consented viewership and engagement data across multiple screens, helping businesses understand audience behaviour, measure performance, and make data-driven decisions.

Responsibilities:

• Own analytical projects end to end, from stakeholder questions and scoping to actionable recommendations.
• Analyse large, complex datasets to identify audience and viewership trends and assess their commercial impact.
• Measure and report data coverage, consistency, and quality; flag material gaps to Data Science and Data Engineering.
• Define and maintain product and business metrics with cross-functional stakeholders.
• Support experiment design and interpret results, separating meaningful signals from noise.
• Build and maintain dashboards for self-service analytics.
• Write production-quality, documented SQL and Python, versioned in Git.
• Clearly document methodologies and assumptions.
• Communicate insights to technical, commercial, and senior stakeholders.
• Collaborate with Data Science, Data Engineering, Product, and commercial teams on analytical requirements and data models.

Must-haves:

• Bachelor’s degree in a quantitative field or equivalent practical experience.
• 3+ years in analytics, insights, or product analytics, independently owning analytical projects.
• Advanced SQL (complex joins, window functions, query optimization).
• Experience with billion-row datasets using Spark, Databricks, or similar.
• Strong Python for statistical analysis and reproducible workflows.
• Solid applied statistics, including sampling and hypothesis testing.
• Dashboard/data visualization experience for technical and non-technical audiences.
• Git and collaborative code review experience.
• English: Upper-Intermediate+.

Nice-to-haves:

• A/B testing or incrementality measurement.
• Knowledge graphs, taxonomies, or product/entity hierarchies.
• dbt, semantic layers, or self-service analytics modeling.
• ML concepts such as propensity, lookalike, or classification models.
• GDPR, consent, clean rooms, or privacy-preserving practices.
• TV measurement vendors (Nielsen, Comscore, iSpot, TiVo, Kinetiq).
• AI assistants in analytical workflows.

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