Sr. Manager Data Science (Dark Stores Intelligence)
The Senior Manager will lead a team of data scientists to develop and deploy machine learning solutions for dynamic pricing, demand forecasting and operational intelligence within Delivery Hero's Dark Stores network. They will collaborate with engineers, product and business stakeholders to translate data insights into scalable models that improve price optimization and efficiency.
Skills required
About this role
Role Summary
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 Manager Data Science of the Global QC Hub, you will be leading and managing our talented ML team in its pursuits to improve the price optimization and boost operational efficiency. You’ll be working closely with Data Engineers and Data Scientists across the larger data team and in collaboration with product and business managers across Talabat and Delivery Hero.
You will own the entire ML workflow, from identifying meaningful opportunities to deployment (e.g. dynamic pricing, uplift modelling, counterfactuals, assortment, personalization, demand forecasting, promo optimization, operational intelligence). You will also lead our causal inference and experimentation practice, so that the impact of every model and decision is measured rigorously.
What's On Your Plate?
Develop a deep understanding of the processes that make up QC business products and operations.
Develop a deep familiarity with QC’s data platforms and dense data ecosystem.
Leverage your understanding of QC business and data combined with your deep understanding of ML to identify opportunities to leverage ML to realize massive business value.
Set the team's roadmap, success criteria, and priorities in close collaboration with engineering and business partners.
Own the entire ML workflow including problem formulation, learning task definition, dataset development, modeling approach selection, model training and refinement, evaluation, and deployment to production.
Lead the experimentation and causal inference practice: design and review A/B, multivariate, switchback, and geo experiments, and apply causal methods (incrementality, uplift modelling, matching) to measure true business impact.
Own data analytics, data engineering, data modeling, and reporting functions.
Own the design and development of our ML platform including feature management, continuous training, serving, and integration for both online and batch inference use cases.
Participate in and lead key technical and design decisions as well as oversee the implementation and deployment of ML-based solutions.
Collaborate effectively with business, product, and engineering managers through the lifecycle of ML and data products.
Maintain a balance between building sustainable practices and going after high-impact projects on one hand and identifying low cost, high impact wins and intelligent scoping to deliver value sooner on the other.
Coach and mentor our ML engineers, data scientists, data engineers technically and professionally.
Develop, maintain, and manage the end to end data analysis, data engineering, and ML development processes.
Drive the recruitment and interviewing of outstanding data talent.
Contribute to the company's overall strategy and tech vision.
Play a key role in continuing to develop our organizational structures to keep up with the scale.
Qualifications
What Did We Order?
Technical Experience
Expert in both classical machine learning and deep learning.
Strong experience with classical ML frameworks.
Strong experience with deep learning frameworks .
Familiarity with classical data structures and algorithms.
Experience deploying, and managing production ML systems at scale.
Experience in recommendation, personalization, search, or logistics problems.
Experience in process optimization or workflow automation is a plus.
Strong background in experiment design and statistical analysis, with the ability to run and analyze A/B and multivariate tests.
Strong causal thinking and hands-on experience with incrementality measurement and uplift modelling.
Experience with advanced experimentation and causal methods, such as switchback testing, geo testing, marketing mix modelling (MMM), and matching (e.g. PSM).
Experience with data pipelines, data modeling, and data reporting is a plus.
Qualifications
Bachelor's degree in engineering, computer science, technology, or similar. A postgraduate degree is a plus but not required.
10+ years of overall experience working in software engineering and data with experience spanning multiple data disciplines.
8+ years of experience 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.
At talabat, your success is defined by both results and behaviors. We hire for excellence in craft and for the Leadership Principles that shape how we think, decide, and collaborate. What you achieve matters, and how you achieve it defines us. Together, they move the business forward and deliver great experiences. Learn more about our Leadership Principles here.
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