Data Scientist - Marketing

We power people’s progress.

At Preply, we’re all about creating life-changing learning experiences. We help people discover the magic of the perfect tutor, craft a personalised learning journey, and stay motivated to keep growing. Our approach is human-led, tech-enabled - and it’s creating real impact.

We’ve just reached unicorn status with a $150M Series D, accelerating our vision to transform education through human-led, AI-enhanced learning. Today, 100,000+ tutors teach 90+ languages to learners in 180 countries - and we’re only getting started. As a category-defining company, we’re shaping what the future of learning looks like at global scale.

Every Preply lesson sparks change, fuels ambition, and drives progress that matters. Joining Preply means helping define the future of education at global scale, and building something that truly matters for millions of people, every day.

Meet the team!

At Preply, we're growing a talented Data Team to empower top-notch decision-making across our organization. Our team collaborates closely with various other chapters - including product, business, and platform - leveraging analysis, experimentation, causal inference, and machine learning.

You’ll be embedded in cross-functional teams, partnering with our MarTech Product Manager, Marketeers, Tech Leads, and the Data Strategy team. Check out our Tech Radar to see the tools and technologies we love using at Preply!

You’ll own Preply’s Marketing Mix Model end to end, the system that governs how we allocate marketing’s largest cost line, a 9-digit annual spend, across more than 30 markets and channels. You’ll partner directly with Finance to turn model output into the budget steering process, and with performance marketing to translate it into channel strategy.

Within a year, you’ll have built a hierarchical model spanning our top markets, calibrated where possible against existing geo-experiments, and actively shaped the measurement roadmap by identifying, designing, and running the next tests needed to further calibrate it. The model’s channel-level marginal ROI will be a direct input into Finance’s 2028 budget planning.

Our Principles

• Hands-on MMM experience: you know that getting it right means testing multiple approaches before converging on a robust, defensible model.
• Strong grounding in Bayesian statistics, applied with rigor to MMM and causal inference.
• Built or owned at least one of: a marketing mix model, a geo-experimentation program, or another causal measurement system that steered real budget, with experience turning its outputs into action in language marketing and finance can use.
• Experimentation expertise: designing and evaluating incrementality tests and A/B experiments.
• Strong Python and SQL skills.
• Advanced written and verbal English.
• PhD in mathematics, economics, statistics or similar, or equivalent applied experience
• Previous experience with causal inference techniques (e.g. geo-lift testing, causal impact, synthetic control, difference in differences, etc.)
• Previous experience with tools such as PyMC, Stan, Meridian, Robyn or similar
• Experience in marketplaces or digital businesses, ideally in performance marketing.