Senior Data Scientist
Pubblicato il 02-10-2026 - Scrambly in Roma
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.
We’re building the future of app discovery: a reward-powered alternative to the App Store and Google Play. Our loyalty-driven ecosystem connects millions of users with the world’s top apps and delivers unmatched, ROI-focused growth for mobile advertisers.
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.
About The Role 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.
The centre of the role is predicting user and cohort value — forecasting what people will be worth from early signal,
and keeping those forecasts accurate as the product and the market move underneath them. 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. We will go through the specifics with you during the process.
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. We are flexible on level. The title reflects the ownership and independence the role carries, not a hard experience bar — we will set the level, title, and compensation to match what you bring.
Key Responsibilities Value prediction