Sr. Data Scientist - Analysis (Logistics & QC)

Requirements

• Bachelor's degree in engineering, computer science, technology, or similar fields. A postgraduate degree is a plus but not required.
• 5+ years of overall experience working in data science and machine learning.
• Experience doing data science in an online consumer product setting is a plus.
• What We'll Be Looking For
• Throughout the interview process, we'll assess not only your technical expertise and problem-solving abilities, but also the behaviors that drive success at talabat:
• Own It : Taking accountability, demonstrating initiative, and driving outcomes end-to-end.
• Deliver Value Fast: Balancing speed and quality to create meaningful business impact.
• Dive Deep: Approaching problems with curiosity, analytical rigor, and a strong attention to detail.
• Bring Good Vibes: Building strong relationships, collaborating effectively, and contributing positively to team culture.
• Raise the Bar: Striving for excellence, challenging assumptions, and continuously improving the quality of your work.
• Stay Humble: Remaining open to feedback, learning from others, and embracing a growth mindset.

Benefits

Additional Information

As the leading delivery company in the region, we have a great responsibility and opportunity to impact the lives of millions of customers, restaurant partners, and riders. To realize our potential, we need to advance our platform to become much more intelligent in how it understands and serves our users. As a Senior Data Scientist supporting our Logistics and QCommerce domains, you will drive high-impact decision-making across business functions through data-driven insights and experimentation. Partnering closely with Business Leaders, and fellow Data professionals, you will take ownership of a key business area and influence strategy through rigorous analysis and measurement. Your scope will span the full data lifecycle, from instrumentation and data modeling to analytics, reporting, experimentation, and delivering actionable recommendations that drive measurable business outcomes. WHAT'S ON YOUR PLATE? Leveraging ambiguous business problems as opportunities to drive objective criteria using data. Developing a deep understanding of the product experiences and business processes that make up your area of focus. Developing a deep familiarity with the source data and its generating systems through documentation, interacting with the engineering teams, and systematic data profiling. Contributing heavily to the design and maintenance of the data models that allow us to measure performance and comprehend performance drivers for your area of focus. Working closely with product and business teams to identify important questions that can be answered effectively with data. Delivering well-formed, relevant, reliable, and actionable insights and recommendations to support data-driven decision making through deep analysis and automated reports. Designing, planning and analyzing experiments (A/B and multivariate tests). Supporting product and business managers with KPI design and goal setting. Technical Experience Excellent SQL. Competence with reproducible data analysis using Python or R. Familiarity with data modeling and dimensional design. Experience designing and analyzing experiments using A/B testing, multivariate testing, switchback experiments, and synthetic control methods. Strong command over the entire data analysis lifecycle including; problem formulation, data auditing, rigorous analysis, interpretation, recommendations, and presentation. Familiarity with different types of analysis including; descriptive, exploratory, inferential, causal, and predictive analysis. Deep understanding of the various experiment design and analysis workflows and the corresponding statistical techniques. Familiarity with product data (impressions, events, ..) and product health measurement (conversion, engagement, retention, ..). Familiarity with BigQuery and the Google Cloud Platform is a plus. Data engineering and data pipeline development experience (e.g. via Airflow) is a plus. Experience with classical ML frameworks (e.g. Scikit-learn, XGBoost, LightGBM, ...) is a plus.

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