Data Scientist II - Analysis, QC

Since launching in Kuwait in 2004, talabat has become the region’s leading on-demand delivery app, serving millions of customers across eight countries. Our Quick Commerce (QC) Hub powers the grocery and convenience delivery experience — getting everyday essentials to customers’ doors in minutes, not hours.Behind every delivery is data. Our QC data team works at a scale most data scientists only read about: millions of daily transactions, thousands of partners, and decisions that directly shape how people across the Middle East get their groceries delivered. We’re building an analytics-first culture where every product and business decision is grounded in evidence.Job DescriptionWhy This RoleAs a Data Scientist on the QC Hub team, you’ll be the analytical brain behind one of talabat’s fastest-growing verticals. 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:40% on deep analysis and experimentation — designing A/B tests, running multivariate experiments, doing deep dives into performance drivers, and turning findings into clear recommendations.30% on data modelling and quality — building and maintaining the data models that let us measure what matters, profiling source data, and ensuring data reliability.30% 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’llTurn 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-HavesStrong SQL skills — you can write complex queries with window functions, CTEs, and optimise 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 health3+ years in data science, analytics, or a related quantitative fieldNice-to-HavesExperience with BigQuery and Google Cloud PlatformData engineering skills (Airflow, dbt, or similar pipeline tools)Experience with ML frameworks (Scikit-learn, XGBoost, LightGBM)Familiarity with modern data tools and AI-assisted analysis workflowsExperience in an online consumer product or marketplace environmentAdditional InformationWhat We OfferImpact at scale — your work directly affects how millions of people get their daily essentialsAn analytics-first culture — a data team that’s genuinely invested in analytical excellence, not just dashboardingModern tooling — BigQuery, GCP ecosystem, and the freedom to experiment with new approaches including AI-assisted workflowsCareer growth — past data scientists on the team have grown into senior individual contributor roles and analytics leadsGreat Place to Work — talabat is a certified Great Place to Work across multiple countries in the region