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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, high-ambiguity role focused on designing and implementing data-driven solutions to optimize marketing ROI across paid channels (Google, Meta, and others).
Key Responsibilities:
• Design and own the Creative Analytics Framework: Build creative performance modeling to separate what a creative actually causes from algorithmic delivery decisions. Drive findings into action with creative and media-buying teams, and automate detection, reporting, and action via MarTech.
• Cohort Maturity Prediction: Develop conversion-lag prediction models to minimize steering delay—predict how today's clicks will convert and connect current spend to future conversions.
• Bidding Signal Optimization: Partner with channel managers and the Applied AI team to improve predicted conversion value sent to Google and Meta. Iterate on which funnel events to optimize and what predictions to attach, balancing signal quality with algorithmic learning.
• Audience Optimization: Create customer uplift models to identify incremental value. Build audiences that improve marketing efficiency by telling networks who to target and who to exclude, feeding prospecting seeds, remarketing audiences, and exclusion lists.
• Cross-functional Collaboration: Work closely with Data Engineering, Applied AI, marketing measurement, MarTech, tracking, and CRM teams on scalable data pipelines, ETL evolution, and new tools/frameworks.
You will define success metrics, design modeling approaches, and turn business hypotheses into testable experiments. Much of this work is untapped territory with no off-the-shelf solutions—you will invent approaches and convince stakeholders of their validity.
Requirements:
• Proficiency in Python, SQL, and distributed frameworks (Spark; Databricks is used at Preply).
• 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 is essential.
• Advanced modeling skills: propensity models to value earlier funnel events and refine targeting; uplift models to identify incremental value.
• Strong causal inference and experiment design expertise. Ability to separate what a channel drives from what it takes credit for, and design tests to settle ambiguity when platforms control delivery.
• Instinct to translate business hypotheses into testable questions, with honesty about data limitations.
• Comfort working across teams you don't control, with strong collaboration skills.
• Background in marketing data science, ads ML, recommender systems, or applied ML roles where model outputs fed other algorithms.
