SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
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 function. This is a high-impact role focused on designing and implementing data-driven solutions to maximize ROI across paid marketing channels (Google, Meta, and others). You'll work at the intersection of marketing, product, and platform teams, tackling largely unsolved problems in marketing analytics and bidding optimization.
Key responsibilities include:
**Creative Analytics Framework**: Design and own a creative performance modeling system that isolates the incremental impact of creative assets from algorithmic delivery decisions. Drive findings into actionable decisions with creative and media-buying teams, and automate detection, reporting, and action through MarTech systems.
**Cohort Maturity & Conversion-Lag Prediction**: Minimize steering delay by predicting how many of today's clicks will convert, enabling real-time connection of 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 signals 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 & Uplift Modeling**: Build customer uplift models to identify incremental value, then translate findings into prospecting seeds, remarketing audiences, and exclusion lists for the networks.
**Cross-Functional Collaboration**: Work closely with Data Engineering, Applied AI, marketing measurement, MarTech, tracking, and CRM teams to build scalable data pipelines and evolve ETL processes.
This role requires comfort with ambiguity and a genuine interest in the marketing bidding universe. Much of the work is untapped territory—you'll invent approaches, validate them with data, and convince stakeholders of their merit.
**Requirements:**
- Proficiency in Python, SQL, and distributed frameworks (Databricks/Spark preferred)
- Experience with marketing or attribution data and working knowledge of how bidding platforms consume signals
- Advanced modeling skills: propensity models, value prediction, and uplift modeling
- Strong foundation in causal inference and experiment design, especially in contexts where platforms control delivery
- Ability to translate business hypotheses into testable questions and honesty about data limitations
- Comfort collaborating across teams you don't control
- Background in marketing data science, ads ML, recommender systems, or applied ML roles where model outputs fed downstream algorithms
