Responsibilities

• Own the design, build, and validation of Icon’s measurement models — attribution, incrementality and lift, media mix modeling, forecasting, and audience analytics.
• Set methodology standards across the team: how models are specified, tested, validated, documented, and refreshed.
• Define measurement design for client tests (geo-lift, holdout, matched-market, and in-platform experiments) including power analysis and test readiness criteria.
• Pressure-test results before they reach clients, and own the defensibility of the numbers we publish.
• Evaluate new data sources, vendors, and methodologies, and recommend where Icon should invest.
• Define the analytical data requirements behind measurement (model inputs, custom client data builds, and the transformations that feed them) and specify them clearly for Data Engineering.
• Partner with Data Engineering, who own pipelines, platform architecture, integrations, and production data quality, on what moves from analysis into the production platform.
• Build and prototype analysis in SQL and Python against Snowflake, and hand proven logic to Engineering to productionize rather than maintaining production pipelines directly.
• Own analytical data quality, validating that model inputs and outputs are correct, and raise platform-level issues to Engineering for resolution.
• Establish reusable code, templates, and documentation standards so analyses are repeatable rather than rebuilt each time.

Requirements

• 7+ years in marketing science, media analytics, data science, or a closely related field, with deep hands-on modeling experience.
• Demonstrated ownership of attribution, incrementality and lift testing, and/or media mix modeling in a media or marketing context.
• Advanced SQL and proficiency in Python (or R) for modeling, analysis, and automation.
• Strong grounding in statistics and causal inference — experimental design, hypothesis testing, time-series analysis, and significance.
• Working knowledge of cloud data environments; Snowflake preferred.
• A real understanding of how media is planned, bought, and optimized across linear TV, CTV, and digital.
• Ability to explain complex methodology clearly and credibly to non-technical clients and executives.
• Bachelor’s degree in Statistics, Mathematics, Economics, Computer Science, or another quantitative field.

Preferred

• Experience in a media agency or direct-response marketing environment.
• Experience building or contributing to an analytics product rather than only one-off analyses.
• Experience applying AI or LLM tooling to analytical workflows.
• Familiarity with ACR-based TV measurement, pixel-based digital measurement, or panel-to-national extrapolation.
• Experience with BI and visualization platforms (Tableau, Sigma, or comparable).
• Experience mentoring analysts or leading a small analytical team.