Ready for a challenge?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.About the role:You will play a pivotal role in the Logistics Decision Systems team. You will dive deep into data to design automated decision-making systems that operate in a dynamic, real-world environment. You will move beyond static predictions to build models that understand cause-and-effect, optimizing for long-term network stability rather than just immediate accuracy. You will proactively generate innovative ideas, craft compelling business cases, and skillfully pitch them to key stakeholders.You will also take charge of developing operational policies and control logic that balance efficiency with reliability. You will work on robust validation strategies, ensuring our algorithms perform safely in volatile conditions before they touch the real world. You will utilize tools like simulation and counterfactual analysis to test hypotheses and refine logic. Additionally, you will collaborate with Developers and Operations Research Scientists, empowering them to utilize our predictions effectively to drive maximum impact in our operations.Finally, you will play a pivotal role in our team by fostering collaboration among peers. You will collaborate with Operations Research Scientists to create hybrid systems where Machine Learning guides optimization constraints. You will help bridge the gap between prediction and control, working with engineers to define the experimental environments needed to train and validate these agents.What will you bring to the team?Hard skills:Advanced proficiency in data science and machine learning methodologies, with extensive experience applying these techniques in production environments.Experience with Sequential Decision Making problems in any domain (e.g., dynamic pricing, inventory control, robotics, game AI, or recommendation systems).Methodological Toolkit: Deep proficiency in at least one of the following areas, with a conceptual understanding of the others:
Simulation: Experience using simulation for model validation or data generation (e.g., Discrete Event Simulation, Agent-Based Modeling). You don't need to be a simulation engineer, but you must know how to design experiments and evaluate policies within a simulated environment.
Reinforcement Learning / Control: Familiarity with concepts like MDPs, Bandits, PID, or MPC. You understand the trade-offs between efficiency and stability, and how to optimize for long-term system behavior.
Causal Inference / Optimization: Understanding of counterfactual analysis, safety constraints, or constrained optimization. You can reason about causal effects and safety boundaries in complex systems.Python (scikit-learn, pandas, etc.) in notebooks and pure Python code for production, and strong proficiency in SQL.Strong understanding of software development best practices, including testing, git, code reviews, and model lifecycle management.Working with Docker containers to support reproducible and scalable environments.Soft skills:Systems Thinking: You intuitively understand feedback loops and second-order effects in complex networks.Leadership and mentorship skills, with the ability to guide and develop junior data scientists and foster team collaboration.A holistic project approach, from generating business cases to managing the full lifecycle of models.Ability to generate innovative ideas, test hypotheses rigorously, and pitch business cases to stakeholders.Critical analysis of approaches, assumptions, and business impact, with the ability to challenge and refine strategies for optimal results.Preference for simple, scalable, and effective solutions, particularly in complex projects.Expertise in agile environments with strong collaborative skills, particularly in cross-functional teams.Excellent communication skills, including the ability to present complex data insights and machine learning concepts to a wide range of stakeholders, both technical and non-technical.At JET, this is on the menu: Our teams forge connections internally and work with some of the best-known brands on the planet, giving us truly international impact in a dynamic environment. Fun, fast-paced and supportive, the JET culture is about movement, growth and about celebrating every aspect of our JETers. Thanks to them we stay one step ahead of the competition.Inclusion, Diversity & Belonging:What else are we delivering?
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