London · London, Greater London, United Kingdom
Open 5 months, first seen by us 164 days agoLocation type: HybridFrom the employer’s posting
Employment type: Full-timeFrom the employer’s posting
Department: DataFrom the employer’s posting
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.
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.
Build and automate the Marketing Mix Model, expanding it from US to a global hierarchical setup
Work with Finance and performance marketing to fold MMM results into budget and channel decisions
Design and run geo experiments and other incrementality tests to measure channel ROI
Build a unified framework tying MMM, attribution, and incrementality together
Extend causal inference methods to Finance and Product as a reusable foundation
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.
build it out, grow it from today's US model to a global, hierarchical setup, and turn it into an automated platform that steers budget allocation
partner with performance marketing and finance to fold results into the budget process
design and run incrementality tests (geo experiments and other causal inference methods) to measure incremental ROI
Design a unified measurement framework that ties MMM, attribution, and incrementality together
Extend these causal inference methods beyond marketing, as a reusable foundation for how Finance and Product measure impact
Nice to have: - 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.)
Open 5 monthsWe first saw this posting 164 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
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Day 164
Up longer than most. Similar jobs usually come down between day 8 and day 60.
Well past Preply’s usual: its jobs come down by about day 64.
From 11,783 similar postings that came down, and 21 of Preply’s. Coming down is not always a hire.
29 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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- Experimentation expertise: designing and evaluating incrementality tests and A/B experiments. - Strong Python and SQL skills. - Advanced written and verbal English.
- Extend these causal inference methods beyond marketing, as a reusable foundation for how Finance and Product measure impact. - Work with Data Engineering to build scalable pipelines and evolve our ETL as the modeling stack grows (e.g. adopting Databricks). What you need to succeed:
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Yes. We’ve followed 2 other Staff Data Scientist - Marketing jobs there since June 2026. All 2 are still up.
New Data Scientist postings at the employers we follow are down 4% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
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.
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.
Own our Marketing Mix Model end to end: build it out, grow it from today's US model to a global, hierarchical setup, and turn it into an automated platform that steers budget allocation. Full ownership, not a support role.
Turn MMM outputs into decisions: partner with performance marketing and finance to fold results into the budget process and guide teams on what they mean for strategy.
Steer the testing roadmap that feeds the MMM: use its signals to pick which channels and questions to test, then design and run incrementality tests (geo experiments and other causal inference methods) to measure incremental ROI.
Design a unified measurement framework that ties MMM, attribution, and incrementality together; MMM for big budget calls, incrementality-adjusted attribution for channel splits.
Extend these causal inference methods beyond marketing, as a reusable foundation for how Finance and Product measure impact.
Work with Data Engineering to build scalable pipelines and evolve our ETL as the modeling stack grows (e.g. adopting Databricks).
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.
An open, collaborative, dynamic and diverse culture;
A generous monthly allowance for lessons on Preply.com, Learning & Development budget and time off for your self-development;
A competitive financial package with equity, leave allowance and health insurance;
Not in Barcelona? We offer an attractive relocation package to join us in our Preply Barcelona Hub
Access to free mental health support platforms;
Access to Gympass-partnered wellness and gym centers throughout Spain to promote and support well-being and physical health;
The opportunity to unlock the potential of learners and tutors through language learning and teaching in 175 countries (and counting!).
Care to change the world - We are passionate about our work and care deeply about its impact to be life changing.
We do it for learners - For both Preply and tutors, learners are why we do what we do. Every day we focus on empowering tutors to deliver an exceptional learning experience.
Keep perfecting - To create an outstanding customer experience, we focus on simplicity, smoothness, and enjoyment, continually perfecting it as every detail matters.
Now is the time - In a fast-paced world, it matters how quickly we act. Now is the time to make great things happen.
Disciplined execution - What makes us disciplined is the excellence in our execution. We set clear goals, focus on what matters, and utilize our resources efficiently.
Dive deep - We leverage business acumen and curiosity to investigate disparities between numbers and stories, unlocking meaningful insights to guide our decisions.
Growth mindset - We proactively seek growth opportunities and believe today's best performance becomes tomorrow's starting point. We humbly embrace feedback and learn from setbacks.
Raise the bar - We raise our performance standards continuously, alongside each new hire and promotion. We build diverse and high-performing teams that can make a real difference.
Challenge, disagree and commit - We value open and candid communication, even when we don’t fully agree. We speak our minds, challenge when necessary, and fully commit to decisions once made.
One Preply - We prioritize collaboration, inclusion, and the success of our team over personal ambitions. Together, we support and celebrate each other's progress.
Preply.com is committed to creating an inclusive environment where people of diverse backgrounds can thrive. We believe that the presence of different opinions and viewpoints is a key ingredient for our success as a multicultural Ed-Tech company. That means that Preply will consider all applications for employment without regard to race, color, religion, gender identity or expression, sexual orientation, national origin, disability, age or veteran status.
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London, Greater London, United Kingdom
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