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Preply is a unicorn edtech platform (Series D, $150M) that connects 100,000+ tutors teaching 90+ languages to learners in 180 countries. The company is building human-led, AI-enhanced learning experiences at global scale.
You will join Preply's Data Team as a Staff Data Scientist embedded in the Paid Marketing Optimization chapter. This is a high-impact role focused on designing and implementing data-driven solutions to maximize ROI across paid marketing channels (Google, Meta, etc.). You'll work closely with MarTech Product Managers, marketers, tech leads, and the Data Strategy team to tackle largely unsolved problems in marketing optimization.
Key responsibilities include:
• Design and own the Creative Analytics Framework: build creative performance models that separate what a creative actually causes from algorithmic delivery decisions, enabling prediction of incremental creative efficiency drivers.
• Cohort maturity prediction: develop conversion-lag models to predict how today's clicks will convert and connect spend to future conversions, minimizing steering delay.
• Bidding signal optimization: partner with channel managers and Applied AI to improve predicted conversion value sent to Google and Meta, iterating on which funnel events to optimize and what predictions to attach.
• Audience optimization: create customer uplift models to identify incremental value, build prospecting and remarketing audiences, and feed exclusion lists to ad networks.
• Collaborate with Data Engineering and Applied AI on scalable data pipelines, ETL evolution, and adoption of new tools and frameworks.
This role requires comfort with ambiguity and a genuine interest in the marketing bidding universe. Much of this territory is untapped—you'll invent approaches, validate them with data, and convince stakeholders of your findings.
Requirements:
• Proficiency in Python, SQL, and distributed frameworks (Databricks/Spark).
• Experience with marketing or attribution data and working knowledge of how bidding platforms consume signals (no need to have run campaigns, but genuine interest in platform behavior required).
• Advanced modeling skills: propensity models for funnel refinement and uplift models for incremental value identification.
• Strong causal inference and experiment design capabilities; ability to separate channel-driven value from platform-attributed value.
• Ability to translate business hypotheses into testable questions and honesty about data limitations.
• Comfort collaborating across teams you don't control (Applied AI, marketing measurement, MarTech, tracking, CRM).
• Background in marketing data science, ads ML, recommender systems, or applied ML roles where model outputs fed other algorithms.
