Senior Data Scientist

ph3About The Company /h3 pbScrambly /b 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. /p pWe’re growing 250%+ YoY across revenue, team, product, and technology — fully bootstrapped and profitable. /p pWe’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. /p pOur 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. /p h3About The Role /h3 pWe 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. /p pThe 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. /p pYou 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. /p pThis 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. /p h3Key Responsibilities /h3 ul liValue prediction. Own and improve how we forecast user and cohort value from early behaviour, across our different revenue streams. Improve accuracy where it matters most commercially, and keep the models working as the product changes. /li liBidding and pricing. 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. /li liSegmentation and targeting. Build the scoring and segmentation that lets us treat users, partners, and inventory according to the value they actually deliver. /li liFraud and abuse. Improve the accuracy of our fraud models, and balance catching abuse against wrongly penalising real users. /li liEvaluation. 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. Stay close to your models once they are live, and flag when accuracy starts to slip. /li liCollaboration. 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. /li /ul h3Requirements /h3 ul li4+ years as a data scientist working on production models that drive real decisions. We care more about what you have owned than about the number. /li liHands-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. /li liAdtech 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. /li liStrong Python and SQL, with production experience on a cloud data warehouse — BigQuery preferred. /li liRigour about evaluation: you choose the metric that matches the business objective, you are careful about out-of-sample and point-in-time correctness, and you are willing to say a model is not good enough to ship. /li liExperience 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. /li liCommercial 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. /li liA proactive, self-directed way of working. You will be in a very small team, with a lot of latitude and little process. /li liEnglish at B2+, written and spoken — you will document your work and work daily with an international team. /li /ul h3Nice to Have /h3 ul liBidding, pricing, or budget allocation systems — control loops, bandits, or other approaches to spending under uncertainty. /li liFraud, anomaly detection, or trust safety modelling, especially with weak or incomplete labels. /li liExperimentation and causal inference — A/B design, power analysis, and incrementality measurement. /li liSurvival, hierarchical, or Bayesian methods applied to retention, monetisation, or sparse-segment problems. /li liBQML, Dataform, or dbt, or other experience building models close to the warehouse. /li liDeploying and monitoring models yourself — pipelines, serving, retraining, and drift monitoring. /li /ul h3What We Offer /h3 ul liOwnership of the models that set UA spend, offer payouts, and fraud decisions — work directly attached to revenue, with visible impact within weeks. /li liProblems with real depth: long-horizon forecasting, adaptivity, pricing under uncertainty, and fraud with imperfect ground truth. /li liA 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. /li liA level and compensation matched to what you bring, rather than to the title on the advert. /li liA profitable, bootstrapped company growing 250%+ YoY, where investment in data is justified by results rather than budget cycles. /li /ul /p #J-18808-Ljbffr