[Hiring] Data Scientist @Lingokids

Role Description

As a Data Scientist on the Product Engagement team, your mission is to push our recommendation and personalization capabilities further by solving the complex statistical and methodological problems that sit beyond standard analytics. Where the analyst maps the user journey and identifies opportunities, you are the person who figures out how to act on them at scale - designing the statistical frameworks, causal models, and experimentation strategies that turn behavioral insights into smarter, more effective personalization. You thrive on hard problems, bring rigorous thinking, and are equally comfortable working with product managers and with ML engineers.

• Advance our recommendation and personalization logic by designing and validating new statistical approaches - from improved segmentation models and engagement scoring to causal uplift estimation and multi-touch attribution.
• Own the experimentation framework for the team: design rigorous A/B and multivariate tests, develop methodologies to handle complex measurement challenges (novelty effects, interference, long-term effects), and ensure results are interpreted correctly.
• Tackle complex analytical problems that require going beyond descriptive statistics - building predictive models, running causal inference analyses, and decomposing metric movements into their drivers.
• Partner closely with the Senior Analyst to turn user journey insights into statistically grounded targeting and personalization strategies.
• Collaborate with the ML Engineering Data Scientist to ensure that statistical models and experimental findings can be produced and integrated into the recommendation engine.
• Define and refine the metrics and success criteria used to evaluate personalization quality, ensuring we measure what actually matters for users and for the business.
• Own the offline evaluation framework: build and maintain counterfactual evaluation methods, replay-based policy estimation, and offline screening pipelines that let the team assess 10 ideas offline for every 1 tested live - dramatically accelerating iteration speed.
• Map and measure the interplay between editorial rules and personalization logic - systematically investigating when and how rules like hardcoded IP placements, content diversity constraints, or curated onboarding slots interact with ML-optimized recommendations, and designing experiments to quantify the trade-off.
• Communicate complex statistical findings clearly and confidently to non-technical stakeholders, and advocate for methodological rigor across the team.

Qualifications

• Strong statistical foundation: deep expertise in probability, statistical inference, regression modeling, and causal inference methods (A/B testing, difference-in-differences, propensity score matching, uplift modeling).
• Python for data science: fluent in Python (Pandas, Numpy, Scipy, Statsmodels, Scikit-learn) for analysis, modeling, and prototyping.
• SQL and data pipelines: strong SQL skills for large-scale data warehouses (Databricks or equivalent).
• Experimentation expertise: has designed and run A/B tests end to end in a product context.
• Recommendation and personalization thinking: experience evaluating or improving how a system decides what content, products, or actions to surface to users.
• Complex problem-solving: comfortable sitting with ambiguous, open-ended problems and structuring a rigorous path to an answer.
• Product and business thinking: connects statistical work to business outcomes.
• Collaboration: works effectively at the intersection of analytics, product, and engineering.

Requirements

• Experience building offline evaluation frameworks for ranking or recommendation systems (counterfactual evaluation, replay estimation, interleaving).
• Experience with recommendation systems or content personalization from a statistical/modeling perspective.
• Familiarity with Bayesian inference or probabilistic modeling.
• Experience with experimentation platforms such as Amplitude or GrowthBook.
• Background in subscription or freemium product analytics.
• English is a must: fluency is essential for effective communication.

Benefits

• Career Growth: Invest in your development up to €2,000 per year for books and training.
• Remote-Friendly: Work from where you’re most productive, home or our offices in Madrid, anywhere within a 2-hour difference from Spain (GMT+1).
• Stock Options: Receive stock options, giving you the opportunity to own part of the company.
• Home Office Setup: €400 allowance for setup and €35/month for remote work expenses.
• Meal Allowances: €60/month on your Cobee card for meals at restaurants or food delivery.
• Flexible Compensation: Manage your meal, transport, and childcare expenses easily with Cobee.
• Health Insurance: Access private health coverage at exclusive rates through Adeslas.
• Language Lessons: Enjoy free language classes in Spanish and English.
• Visa Sponsorship: We’ll handle the process and cover the costs if you need a visa to work in the EU.
• Company events: Team gatherings and off-sites in different corners of Spain.