Descripció del projecte
This is an exciting project to develop a cutting-edge logistics optimization engine to streamline feed supply chain operations. To achieve this, we are looking for a highly qualified and research-oriented Operations Research Engineer to lead the development of this critical system and become a key member of our team.
The candidate will design and implement state-of-the-art optimization algorithms to address logistical challenges in the animal feed supply chain. This is a hands-on role that requires strong experience in research, mathematical modeling, and algorithm development, with a focus on translating complex mathematical concepts (routing, inventory management, forecasting) into a robust, scalable, and well-structured software product.
Key responsibilities:
Development of algorithms and models:
– Design and implement optimization algorithms for route planning, inventory management and supply chain planning.
– Develop mathematical models to simulate and optimize specific logistics processes in the animal feed sector.
– Ensure that solutions are scalable and adaptable to different operational scenarios.
– Develop and use digital twins and simulations to evaluate, refine and validate optimization models
Integration and Collaboration:
– Work closely with our software development team to integrate algorithms into a functional optimization engine.
– Collaborate with the product team to ensure that algorithms align with user needs and industry requirements.
Data analysis and visualization:
– Analyze operational data to identify inefficiencies and propose algorithmic solutions.
– Leverage machine learning or AI techniques when relevant to improve optimization results.
Innovation and research:
– Stay up to date with advances in operational research, logistics and supply chain optimization.
– Promote innovation by experimenting with new techniques and methodologies.
Performance evaluation and iteration:
– Develop metrics to evaluate the effectiveness of algorithms.
– Continuously refine solutions based on real-world performance results and feedback.
