Applied Scientist, SCOT FO

Vedi l’offerta su Go Lavoro: golavoro.it/annuncio-lavoro/85fphy

Job ID: 10556295 | Amazon EU SARL (Spain Branch)
La descrizione completa del lavoro illustra tutte le competenze, l'esperienza pregressa e le qualifiche che i candidati dovrebbero possedere.
How does Amazon decide which fulfillment center ships your order, which truck carries it, and how to keep promises across hundreds of millions of packages daily? How does it decide how many trucks and how much labor are required to ship orders across the network?
SCOT Fulfillment Optimization (FO) owns the optimization and forecasting science behind these decisions.
We are seeking Applied Scientists to join the FO Science & Tech team in Barcelona (alternatively: Luxembourg or London) with a strong academic background in optimization, machine learning, and/or time-series forecasting.
- You will design and build state-of-the-art machine learning and optimization models that power Amazon's fulfillment decisions at an unprecedented scale across two core scientific pillars:
- Large-Scale Optimization and Planning: Designing planning systems for order assignment and resource utilization,



while balancing multi-objective cost-speed tradeoffs to enable controllers to steer millions of shipments per hour optimally.
- Demand Forecasting & Predictive ML: Developing time-series forecasts for customer demand, incorporating contextual information (weather, sales, order properties), and modeling uncertainty for core planning systems.

Basic qualifications
- PhD in Operations Research, Applied Mathematics, Computer Science, or related field (or equivalent experience)
- Strong programming skills (Python preferred; experience with optimization solvers a plus)
- Research experience in one or more:
- Large-scale mathematical programming (LP, MIP, decomposition methods)
- Combinatorial optimization (assignment, scheduling, network flows)
- Multi-objective optimization and control
- Large-scale time-series forecasting (GenAI models, probabilistic forecasting, uncertainty quantification)
- Causal infer

Vedi l’offerta su Go Lavoro: golavoro.it/annuncio-lavoro/85fphy