Sr. Manager Data Science - Dark Stores Intelligence

About the role

talabat, the on-demand food and Q-commerce app, is hiring a Senior Manager Data Science for its Global QC Hub. You will lead and manage the ML team focused on improving price optimization and operational efficiency, working closely with Data Engineers and Data Scientists and with product and business managers across Talabat and Delivery Hero. You will own the full ML workflow, from problem identification to deployment, and lead the causal inference and experimentation practice.

Key responsibilities

• Set the team's roadmap, success criteria and priorities with engineering and business partners.
• Own the ML workflow: problem formulation, task definition, data preparation, model selection, training, evaluation and deployment to production.
• Lead experimentation and causal inference: design and review A/B, multivariate, switchback and geo experiments, and apply incrementality, uplift modelling and matching to measure true impact.
• Own data analytics, data engineering, data modeling and reporting, and the ML platform including feature management, continuous training, serving and online and batch inference.
• Coach and mentor ML engineers, data scientists and data engineers, and drive recruitment and interviewing of data talent.
• Drive the end-to-end data analysis, data engineering and ML development processes, and contribute to the company's strategy and organizational structure.

Required profile

• Bachelor's degree in engineering, computer science, technology or similar; a postgraduate degree is a plus.
• 10+ years of overall experience in software engineering and data across multiple disciplines.
• 8+ years in machine learning at a high-growth consumer product company or similar.
• 3+ years of engineering management experience in ML engineering or a related area.

Required skills

• Expertise in classical machine learning and deep learning, with strong experience of their frameworks.
• Production ML systems at scale, and experience in recommendation, personalization, search or logistics.
• Strong background in experiment design and statistical analysis, including A/B and multivariate tests.
• Causal methods such as incrementality, uplift modelling, switchback testing, geo testing, MMM and matching (e.g. PSM).
• Data pipelines, data modeling and reporting (a plus).