About The Company Scrambly is one of the fastest-growing adtech startups in the world, featured in the AppsFlyer Performance Index and Singular ROI Index in just 3.5 years. We're growing 250%+ YoY across revenue, team, product, and technology — fully bootstrapped and profitable. Our mission is to build a true alternative to traditional app stores — fueled by rewards and data — and create a new, performance-first growth engine for mobile app advertisers.

We are looking for a Senior Data Scientist to own the models behind how Scrambly spends and makes money — what our users are worth, what we should pay to acquire and reward them, and how we keep the ecosystem free of abuse. Around it sits a set of connected problems across marketing, monetisation, and risk, each of which turns a prediction into a decision the business acts on.

You will own the modelling end to end: framing the problem, building the model, deciding how it is evaluated, and staying with it once it is live. You will not be doing that alone — you will work closely with our engineers on data, pipelines, serving, and integration. This is an early hire on a data team being built from scratch, reporting to and working day to day with the Head of Data.

Improve accuracy where it matters most commercially, and keep the models working as the product changes. Build the modelling that decides what we are willing to pay to acquire users, together with the controls that keep spend inside margin limits while the system is still learning. Build the scoring and segmentation that lets us treat users, partners, and inventory according to the value they actually deliver.

Decide how each model is judged and hold that line — out-of-sample discipline, backtesting, and choosing metrics that reflect the commercial objective rather than the convenient one. Work with the Head of Data on prioritisation, with our engineers on data pipelines and serving,



and with commercial teams on turning model output into decisions they trust. 4+ years as a data scientist working on production models that drive real decisions. Hands-on experience with LTV or ROAS prediction — forecasting user or cohort value from early behaviour, and dealing with the problems that come with it: immature cohorts, long horizons, sparse segments, and models that decay as the product changes.

This is the core of the role and we are looking for someone who has done it before. ~ Adtech or mobile performance marketing background — attribution and MMP data, cohort and campaign economics, and how UA spend actually gets decided. Backgrounds in gaming, gambling, or user-incentive and loyalty systems are equally relevant, and experience with rewarded, offerwall, or incentivised apps is a significant advantage. ~ Strong Python and SQL, with production experience on a cloud data warehouse — BigQuery preferred. ~ Experience of models that made it into production and were used to make real decisions — you understand what it takes to get there and what happens to a model afterwards. ~ Commercial judgement and clear communication — able to explain a model to people who will spend money based on it, and to push back when the data does not support the question being asked. ~ English at B2+, written and spoken — you will document your work and work daily with an international team. Bidding, pricing, or budget allocation systems — control loops, bandits, or other approaches to spending under uncertainty.

Experimentation and causal inference — A/B design, power analysis, and incrementality measurement. Deploying and monitoring models yourself — pipelines, serving, retraining, and drift monitoring. A close working relationship with the Head of Data, a real say in how the data function is built, and room to grow as the team expands.

A profitable, bootstrapped company growing 250%+ YoY, where investment in data is justified by results rather than budget cycles.

📌 Data Science Manager (Remote) (Madrid)
🏢 Scrambly
📍 Madrid