Staff Data Scientist

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, data is at the heart of every decision we make. We run hundreds of A/B tests to continually optimize our product, each with its own analytical and tracking challenges. The complexity of our subscription model, along with the unique dynamics of tutor-learner interactions, offers an exciting opportunity for those looking to make a real impact.

As a Staff Data Scientist , you will own the data strategy for a critical product domain (e.g., Growth, Retention, Marketplace) and act as the senior analytical authority in that space. You will partner with Directors and VPs across Product, Engineering, and Business to shape long-term strategy, influence investment decisions, and ensure our roadmap is grounded in rigorous, scalable measurement frameworks.

Our Data Team is dedicated to empowering top-quality decision-making. Do you want to know how? Visit our Tech Radar to learn about the technologies we use at Preply!

What you’ll be doing:

Develop and formalize a strategic understanding of the key value drivers of your domain, including user behavior, marketplace dynamics, pricing, and long-term unit economics - and translate these into actionable decision frameworks used by leadership.

Define the long-term measurement and experimentation strategy for your domain, identifying structural growth levers and step-change opportunities beyond incremental optimization.

Design the experimentation programme in your domain and execute, and evaluate large-scale experiments to test new ideas and measure their effectiveness in driving business outcomes.

Lead deep analytical investigations into customer behavior and retention dynamics, reframing ambiguous business questions into structured analyses that drive step-change improvements in long-term value.

Own impact modeling and scenario analysis for major product initiatives, enabling quarterly and annual planning decisions through robust forecasting, trade-off analysis, and risk assessment.

Own the definition and governance of key metrics within your domain, ensuring clarity, consistency, and alignment with company-level objectives.

Act as a senior thought partner to product, engineering, and data leaders, driving alignment across teams and elevating analytical standards across the organization.

Lead cross-domain analytical initiatives (e.g., company-wide LTV modeling, pricing strategy, marketplace health framework).

What you need to succeed:

Demonstrated track record of materially influencing product or business strategy in complex environments, with clear examples of company-level impact.

Expertise in experimentation and causal inference, including operating under imperfect conditions, resolving ambiguity, and guiding high-stakes decisions where clean experiments are not always feasible.

Strong understanding of subscription and marketplace economics, including LTV modeling, cohort dynamics, retention mechanics, pricing trade-offs, and long-term value optimization.

Strong technical proficiency (e.g., SQL, Python) with the ability to review and elevate the analytical work of others, set coding standards, and design scalable analytical workflows.

Experience mentoring and technically guiding senior data scientists, contributing to the overall growth and maturity of the data organization.

Curiosity, problem-solving and critical-thinking skills, as well as the ability to proactively identify and address challenges.