Imagine a role where your models don’t just sit on a server—they shape everyday life. You’ll join a tight-knit squad of 5 (within a growing chapter of 13) to use advanced machine learning and AI for smarter, more efficient e-grocery operations. We’re growing fast and there’s large potential to shape your own journey. While we move quickly to enhance our MLOps processes and deliver rapid results, we also value broader strategic thinking and encourage you to keep the big picture in mind. You’ll develop forecasting models that help warehouses plan better, reduce waste, and keep shelves stocked with fresh products. You’ll automate decision-making in supply chains, pricing and other commercial functions using tools like Keboola and Google Cloud Platform (GCP) to build robust ML pipelines. If you’re ready to tackle real-world challenges and see the impact of your work every day, this could be the perfect place for you.
Forecast & Optimize
Develop and fine-tune forecasting models that drive warehouse planning, demand prediction, and supply chain efficiency.
Automate Decisions
Use AI and machine learning methods—along with LLM APIs—to minimize manual intervention in daily operations.
Integrate Seamlessly
Deploy, scale, and maintain ML pipelines using Keboola or GCP, ensuring high availability and performance.
Incorporate MLOps & CI/CD
Implement best practices for continuous integration, continuous delivery, and reliable model deployment under tight deadlines.
Collaborate & Innovate
Partner with various teams to identify high-impact use cases for ML and AI, especially in supply chain, personalization, pricing, and planning.
Balance Speed & Vision
Deliver quick, pragmatic solutions while considering broader strategic goals. Rapid iterations are crucial, but so is maintaining a forward-looking approach.
Time-Series & Demand Forecasting
Proven background in building models for real-world demand forecasting or similar time-series challenges. Ability to integrate LLM-based methods for enhanced predictions is a plus. Experience with Bayesian or graph-based approaches (e.g., PyMC) is highly desirable, similarly a degree in Mathematical Statistics.
Technical Proficiency
Strong in Python, SQL, and machine learning/deep learning libraries. Familiarity with deploying ML solutions to production environments on GCP is preferred. Knowledge of CI/CD and MLOps is a plus.
Problem-Solving Mindset
Demonstrated ability to translate data and algorithms into impactful, pragmatic solutions; able to prioritize effectively and prefer “done” over “perfect.”
Adaptability & Curiosity
Ready to learn new tools quickly and stay up-to-date on emerging ML/AI trends, even in a fast-paced setting.
Fluent English
Comfortable working in an international team and explaining technical details to non-technical stakeholders.
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