Data Scientist II - Analysis and Quality Control

About talabat and the QC Hub

Since its inception in Kuwait in 2004, talabat has evolved into the premier on-demand delivery platform in the region, catering to millions across eight countries. Its Quick Commerce (QC) Hub facilitates rapid grocery and essentials delivery, ensuring customers receive everyday items within minutes.

The QC data team operates at an immense scale, handling millions of daily transactions and thousands of partners. The team's mission is to foster an analytics-driven culture where every business and product decision is firmly rooted in robust evidence.

Role Overview

As a Data Scientist on the QC Hub team, you will be at the analytical core of one of talabat's fastest-expanding areas. Beyond crunching numbers, you will collaborate closely with product and business leaders to influence strategy, design impactful experiments affecting millions of users, and build reliable data infrastructure that drives smarter decision-making.

Success Milestones

• Within 90 days: Acquire domain knowledge, establish strong partnerships with product and business teams, understand the data environment, and deliver your initial actionable insights.
• Within 6 months: Become the primary analytical expert for your domain, independently design and analyze experiments, and consistently provide recommendations that stakeholders act upon.
• Within 12 months: Demonstrate measurable enhancements in decision quality within your area, develop or improve data models relied upon daily, and mentor junior team members on analytics best practices.

Key Responsibilities

• Devote about 40% of your time to in-depth analysis and experimentation, including designing A/B and multivariate tests and delivering clear data-driven recommendations.
• Allocate roughly 30% to build and maintain data models, ensure data quality, and profile source data for reliability.
• Spend approximately 30% collaborating with stakeholders to frame the right questions, establish KPIs, and communicate insights that influence decisions.
• Translate ambiguous business queries into structured analytical challenges.
• Develop and manage dimensional data models in BigQuery.
• Design, conduct, and interpret controlled experiments.
• Create automated, user-friendly dashboards and reports for stakeholders.
• Challenge existing assumptions using data and work with data engineers to maintain logging and data pipelines.

Ideal Candidate Attributes

• Enjoy close integration with business teams versus working in data silos.
• Find fulfillment in driving business decisions through analysis, not just reporting.
• Are comfortable navigating ambiguity and transforming vague questions into concrete analyses.
• Prioritize data quality and are eager to explore source systems to understand data meanings fully.
• Communicate effectively with non-technical audiences.

Unsuitable Fit If

• Your passion lies exclusively in building machine learning models rather than analytics and experimentation.
• You prefer independent work without frequent stakeholder interaction.
• You require well-defined problem statements rather than open-ended challenges.
• Your focus is more on tools and methods than on the business impact of your work.

Qualifications

Educational Background: Degree in quantitative disciplines such as statistics, mathematics, economics, computer science, engineering, or similar fields is required. Advanced degrees are advantageous but not mandatory.

Essential Skills and Experience:

• Proficient in SQL with ability to write complex, performance-optimized queries involving window functions and CTEs on large datasets.
• Experienced in reproducible data analysis using Python or R with well-organized and clean code.
• Competent in designing experiments, understanding when to select A/B or multivariate tests, calculating appropriate sample sizes, and aware of statistical pitfalls.
• Demonstrated ability managing the full analytics lifecycle from question framing through data auditing, analysis, interpretation, to sharing actionable recommendations.
• Knowledgeable in data modeling principles, especially dimensional modeling suited for ad-hoc analysis and automated reporting.
• Sound understanding of product analytics, including metrics like conversion, engagement, and retention to assess product health.
• At least three years’ experience in data science, analytics, or related quantitative roles.

Preferred Skills:

• Familiarity with Google BigQuery and Google Cloud Platform.
• Experience with data engineering tools such as Airflow or dbt.
• Exposure to machine learning frameworks like Scikit-learn, XGBoost, or LightGBM.
• Knowledge of modern data tools and AI-driven analytic workflows.
• Experience working within online consumer products or marketplace environments.

Additional Information

• Opportunity to impact millions of customers' everyday grocery delivery experience.
• Work within a culture prioritizing analytical rigor and excellence, beyond mere dashboard creation.
• Use modern data technologies including BigQuery, GCP, and access to experiment with AI-assisted analytical methods.
• Opportunities for professional growth including advancement to senior data scientist or analytics lead roles.
• Work at a company recognized as a certified Great Place to Work in multiple regional countries.