Machine learning engineer in consumer pricing

Ready for a challenge? Then Just Eat Takeaway.com might be the place for you. We're a leading global online delivery platform, and our vision is to empower everyday convenience. Whether it's a Friday-night feast, a post-gym poke bowl, or grabbing some groceries, our tech platform connects tens of millions of customers with hundreds of thousands of restaurant, grocery and convenience partners across the globe.

• Design, develop, and train data science models to solve complex business problems, taking full end-to-end ownership from initial prototyping to production deployment.
• Develop the architectural design of ML systems, building both the core algorithmic training pipelines and the scalable inference and monitoring infrastructure.
• Collaborate extensively along with other Data Scientists, Data Engineers, and Product stakeholders to bridge the gap between model development and production systems.
• Drive the adoption of MLOps best practices (e.g., CI/CD for ML, model versioning, feature stores) to accelerate the feedback loop for Data Scientists.
• Communicate the complexities of ML systems (e.g.,e.g., algorithmic choices, latency vs. accuracy trade-offs) effectively to both technical and non-technical stakeholders.
• Build and maintain a strong network across the Data and Engineering organizations to ensure ML systems integrate seamlessly with the wider platform.

• Strong knowledge of regression, boosting gradient, causal inference data science models, feature engineering and evaluation of model performance.
• Strong experience in machine learning, specifically developing ML infrastructure platforms for consumer pricing and policy-related products.
• Hands-on experience developing data science models (predicting and causal modelling) using Python libraries such as pandas, NumPy, scikit-learn, and TensorFlow.
• Proficiency in Python and a strong understanding of software engineering principles (object-oriented design, design patterns, testing) applied to ML.
• Demonstrable experience designing and operating ML systems in production (beyond notebook-based training), including familiarity with serving patterns such as REST APIs, batch inference, and event-driven architectures.
• Ability to work independently and solve complex problems, with a proven track record of productising and deploying models using cloud-native ML platforms (e.g., AWS SageMaker, GCP Vertex AI).
• Familiarity with orchestration, tracking, and containerisation tools, particularly Airflow, MLflow, and Docker.
• Solid Understanding of data engineering fundamentals (e.g., SQL, distributed data processing) to debug and optimise data flows.

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