Staff Data Scientist, Dmarts - Quick Commerce

Deep analysis. Smarter systems. Real users. As a Staff Data Scientist, you'll lead the forecasting and optimization models that decide how our Dmarts and local shop vendors source and stock their products. You'll set the ML standards for the squads in your domain and be the technical leader behind their models and architecture. Your work will help keep shelves stocked, cut waste, and make our supply chain more capital-efficient.
The team's mission is to improve the vendor experience and bring value to the critical path of the supply chain process through data science and data engineering. You'll work with an international, experienced team, shaping the experience for millions of customers who order daily through our platform.
Be part of redefining how customers experience quick commerce. You'll help build technology that scales our non-food offerings, reaching new market segments and driving revenue growth. By innovating within our Quick Commerce Team, you'll make Delivery Hero the go-to platform for a broad range of products, helping us grow faster and deliver more value to customers around the world.
Develop, test, and productionize forecasting algorithms using machine learning, statistical inference, and optimization models.
Act as the technical leader for ML projects in your domain and own their model and architecture design.
Set ML best practices and standards for the squads in your domain, and drive their adoption.
Review the work of data scientists in your domain and mentor them.
Work with product and engineering leads to set the vision and goals for your squads.
Identify new technologies and opportunities to improve the forecasting and operations systems you build for stakeholders.
9+ years of experience in Data Science designing robust end-to-end data workflows, and at least 5 years of experience with forecasting techniques or optimization.
Proven track record owning the data science lifecycle (design, evaluation, deployment, monitoring) for models in production.
Experience setting ML best practices and driving their adoption across multiple squads.
Solid programming skills in Python, Docker, and cloud platforms (AWS or GCP).
Hands-on experience with AI frameworks (e.g., TensorFlow, PyTorch) and with LLMs.
Ability to build consensus among stakeholders and data scientists, lead without formal authority, and communicate results clearly in English to technical and non-technical audiences.

Employment type
Full-time

Work arrangement
No