Manager Data Scientist – AI – Talabat

Talabat, the leading on-demand food and q-commerce app in the region, is hiring a Manager-level Data Scientist focused on AI, a senior technical role responsible for building and guiding machine learning and AI work that feeds directly into how the platform operates, from how orders are matched and routed to how the broader delivery experience gets optimized at scale.

The role combines hands-on data science leadership with people management, guiding a team’s technical approach to modeling and AI problems, while staying close enough to the detail to make sound calls on methodology, data quality, and what’s actually feasible to ship. Because Talabat operates at genuinely large scale across multiple markets, the AI and data science problems here are rarely academic, they need to work reliably against real, messy, high-volume operational data.

This suits an experienced data scientist who’s ready to lead a team as well as solve hard technical problems personally, comfortable translating ambiguous business questions into well-scoped modeling work, and genuinely energized by applying AI to real operational problems rather than purely research-style work. People who thrive in this kind of role combine strong technical depth with the communication skills to bring non-technical stakeholders along.

Talabat operates a real-time, high-volume marketplace, matching customers, restaurants or stores, and delivery riders, and AI models built by this role directly affect how efficiently that matching happens, which has a measurable effect on delivery times, rider utilization, and ultimately customer satisfaction across every order the platform processes. Because the business operates at the scale Talabat does, even modest improvements in model performance, better demand forecasting, smarter order routing, more accurate ETAs, translate into significant operational efficiency and cost savings across the whole network rather than marginal gains on a small sample. As part of the Delivery Hero Group, Talabat's technical work also contributes to a broader, shared body of AI capability across markets, meaning strong work done here can have influence beyond the immediate region. The people-leadership side of this role also matters commercially: a well-led data science team ships more reliable models faster and with fewer costly production failures, which protects both operational performance and the trust the wider business places in AI-driven decisions as that reliance grows over time.

Leading applied AI work at genuine scale, across millions of real transactions rather than curated academic datasets, is some of the most valuable, resume-defining experience a data scientist can build, because it proves the ability to make models that actually survive contact with messy, high-volume, real-world data. Combining technical leadership with people management also builds a dual skill set that's increasingly in demand as organizations look for data science leaders who can both solve hard problems and build strong teams around them, a combination that opens doors to director-level and VP-level data science roles later. Working within the Delivery Hero Group's broader network also provides exposure to AI practices and talent across multiple markets, not just Talabat's regional operation, broadening both technical perspective and professional network. The operational, business-embedded nature of this role, rather than a purely research-focused data science position, builds strong commercial instincts alongside technical depth, a combination that's particularly valued by companies looking for data science leaders who understand the business, not just the math. For an experienced data scientist aiming toward senior technical leadership, this role offers both the scale and the leadership scope to build a genuinely compelling case for that next step. It also builds a specific, well-regarded credibility that comes from having led AI work that genuinely shipped and mattered, not just research that stayed in a notebook.

• Strong background in data science, machine learning, or applied AI, with a degree in a quantitative field
• Proven experience leading or managing a data science or machine learning team
• Track record of shipping machine learning models into real, high-volume production systems
• Strong Python and SQL skills, with experience in common ML frameworks
• Ability to translate ambiguous business problems into well-scoped technical work
• Excellent communication skills for working with both technical and non-technical stakeholders