Marketing Data Scientist – Media Effectiveness

Role Overview

• Develop advanced performance analyses for paid channels, including Google Ads, Meta Ads, App Campaigns, YouTube, programmatic and other digital channels.
• Evaluate the true efficiency of media investments, considering not only platform metrics but also impact on MQLs, SQLs, sales, revenue, CAC, LTV, lead quality and incremental contribution.
• Identify opportunities to optimize budget across channels, campaigns, products, brands, regions and audiences.
• Build analyses of channel saturation, marginal efficiency, investment elasticity and return curves.
• Support the development of marketing measurement models, including attribution, MMM, incrementality tests, holdouts, geo tests and controlled experiments.
• Analyze the relationship between paid media and other channels, such as organic, direct, branded search, CRM and app, identifying cross-effects and synergy opportunities.
• Act as the technical counterpart to agencies, platforms and media partners, ensuring greater analytical depth in recommendations and decisions.
• Translate complex data into simple, actionable executive recommendations connected to business outcomes.
• Support planning cycles, forecasting, budget reviews and executive presentations with robust analyses on media performance and efficiency.
• Create frameworks, rituals and analytical governance to improve decision-making within the performance team.
• Work collaboratively with Marketing, Data, Product, BI, CRM, SEO, Finance and Business teams to connect media investment to real business impact.

Requirements

• Solid experience in Performance Marketing, Growth Analytics, Marketing Analytics, Marketing Science, Media Effectiveness or related areas.
• Advanced knowledge of digital media and paid channels, particularly Google Ads and Meta Ads.
• Experience with acquisition funnel analysis, conversion, lead quality and revenue impact.
• Strong command of performance metrics such as CPC, CPM, CTR, CPA, CPL, CAC, ROAS, LTV, payback and conversion rate.
• Knowledge of attribution models and their limitations.
• Familiarity with MMM, incrementality tests, holdouts, geo tests or other causal measurement methodologies.
• Strong analytical skills and applied statistics for business.
• Experience with SQL, BigQuery or similar data analysis tools.
• Experience with data visualization tools such as Looker, Tableau, Power BI or similar.
• Ability to translate complex analyses into clear, objective and actionable recommendations.
• Excellent communication and the ability to influence decisions with data.
• Knowledge of Python or R for statistical analysis, modeling or automation (nice to have).
• Experience with MMM and incrementality tools or frameworks such as Robyn, Meridian, GeoLift, CausalImpact or similar (nice to have).

Tech Stack

• BigQuery
• Python
• SQL
• Tableau

Benefits

• Market-competitive compensation
• Health insurance
• Dental plan
• Private pension
• Flexible benefits that adapt to your needs
• Meal allowance
• Financial counseling
• Perks club
• Home office allowance
• Mobility allowance
• Childcare allowance
• Life insurance
• Extended leaves
• Support for sports, quality of life and wellbeing
• Payroll-deductible loans with reduced rates