As a Data Scientist on the QC Hub team, you’ll be the analytical brain behind one of the company'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.
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What You’ll Actually Do
You’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’ll:
- Turn ambiguous business questions into structured analytical problems
- Build and maintain dimensional data models in BigQuery
- Design, execute, and interpret experiments (A/B and multivariate)
- Create automated dashboards and reports that stakeholders actually use
- Challenge assumptions with data — including your own
- Collaborate with data engineers on logging and data pipeline quality
What You Bring
Degree 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:
- Strong SQL skills — you can write complex queries with window functions, CTEs, and optimise for performance on large datasets
- Reproducible analysis in Python or R — you write clean, well-structured analytical code, not one-off scripts
- Experiment design expertise — you understand when to use A/B vs. multivariate tests, can calculate sample sizes, and know the pitfalls of statistical testing
- Full analysis lifecycle experience — from problem framing through data auditing, analysis, interpretation, and presenting recommendations
- Data modelling knowledge — you understand dimensional design and can build models that serve both ad-hoc analysis and automated reporting
- Product analytics intuition — you’re familiar with metrics like conversion, engagement, and retention, and know how to measure product health
- 3+ years in data science, analytics, or a related quantitative field
Nice-to-Haves:
- Experience with BigQuery and Google Cloud Platform
- Data 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 workflows
- Experience in an online consumer product or marketplace environment
Location
Dubai, United Arab Emirates
