Data Scientist

Company Description:talabat is the leading on-demand food and non-food delivery platform in MENA, operating across 8 countries and processing hundreds of millions of orders annually. We're part of Delivery Hero, the global leader in online food delivery and q-commerce, and we're engineering-first.Job Description:As a Data Scientist on the New Ventures team, you’ll be the analytical brain behind the team launching new verticals and products etc across 8 different markets. You won’t just crunch numbers — you’ll partner directly with product and business leaders to shape strategy, design experiments that affect millions of users, and build the data foundations that power smarter decisions.This is a role for someone who loves turning messy, ambiguous business questions into clean, actionable analysis — and who gets energy from seeing their insights change how a team operates.What Success Looks LikeFirst 90 days:You’ve ramped up on your domain, built relationships with your product and business partners, understood the data landscape, and delivered your first actionable analysis.By 6 months:You’re the go-to analytical partner for your domain. You’re independently designing and running experiments, and stakeholders regularly act on your recommendations.By 12 months: Your work has measurably improved decision quality in your domain. You’ve built or refined data models that the team relies on daily, and you’re mentoring newer team members on analytical best practices.What You’ll Actually DoYou’ll spend roughly:50% on deep analysis and experimentation — designing A/B tests, running multivariate experiments, doing deep dives into performance drivers, and turning findings into clear recommendations.20% on data modelling and quality — building machine learning models like predicting Customer Creditworthiness of Postpaid products or recommendation model for Beauty products30% working with stakeholders — partnering with product and business managers to frame the right questions, set meaningful KPIs, and present insights that drive action.Day-to-day, you’ll:Turn ambiguous business questions into structured analytical problemsBuild and maintain dimensional data models in BigQueryDesign, execute, and interpret experiments (A/B and multivariate)Create automated dashboards and reports that stakeholders actually useChallenge assumptions with data — including your ownCollaborate with data engineers on logging and data pipeline qualityYou’ll Thrive Here If You…Love being embedded with business teams, not siloed in a data teamGet satisfaction from changing how decisions are made, not just producing reportsAre comfortable with ambiguity — many of your best projects will start as vague questionsCare deeply about data quality and are willing to dig into source systems to understand what the data actually meansCommunicate clearly with non-technical stakeholdersThis Might Not Be For You If You…Want to build ML models full-time (this role is analytics and experimentation focused)Prefer working independently without regular stakeholder interactionNeed clearly defined problems handed to youAre more interested in tools and techniques than business impactQualificationsWhat You BringEducationDegree in a quantitative field (statistics, mathematics, economics, computer science, engineering, or similar) — or equivalent practical experience. A postgraduate degree is a plus but not required.Must-Haves:2-5 years post-qualification experienceStrong SQL skills — you can write complex queries with window functions, CTEs, and optimize for performance on large datasetsReproducible analysis in Python or R — you write clean, well-structured analytical code, not one-off scriptsExperiment design expertise — you understand when to use A/B vs. multivariate tests, can calculate sample sizes, and know the pitfalls of statistical testingFull analysis lifecycle experience — from problem framing through data auditing, analysis, interpretation, and presenting recommendationsData modelling knowledge — you understand dimensional design and can build models that serve both ad-hoc analysis and automated reportingProduct analytics intuition — you’re familiar with metrics like conversion, engagement, and retention, and know how to measure product health