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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, technically deep role focused on designing and implementing data-driven solutions across paid marketing channels (Google, Meta, and others). You'll work at the intersection of marketing, machine learning, and causal inference—inventing approaches where off-the-shelf solutions don't exist.
Key responsibilities:
**Creative Analytics Framework**: Design and own a creative performance modeling system. Drive findings into decisions with creative and media-buying teams, automate detection and reporting via MarTech, and build models that isolate creative causality from algorithmic delivery to predict incremental drivers of creative efficiency.
**Cohort Maturity & Conversion-Lag Prediction**: Minimize steering delay by predicting how many of today's clicks will convert, connecting current spend to future conversions that haven't yet occurred.
**Bidding Signal Optimization (Value-Based Bidding)**: 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 against algorithmic learning windows.
**Audience Optimization & Uplift Modeling**: 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 results back as 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'll operate in an environment of high ambiguity, turning business hypotheses into testable data questions and having the honesty to say when data can't answer them. This role requires deep technical expertise, marketing domain knowledge, and the ability to influence teams you don't control.
**Requirements:**
- Proficiency in Python, SQL, and distributed frameworks (Databricks/Spark preferred)
- Experience with marketing or attribution data; working understanding of how bidding platforms consume signals (campaign-running experience not required, but genuine interest in platform behavior essential)
- Advanced modeling: propensity models for funnel refinement and uplift models for incremental value identification
- Causal inference and experiment design expertise; ability to separate channel-driven outcomes from platform-attributed credit
- Instinct to translate business hypotheses into testable questions; intellectual honesty about data limitations
- Comfort collaborating across teams without direct 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 downstream algorithms
