Applied Scientist II — ML & Computational Modeling

Amazon; Milano; Ibrido; EUR 123.000 - 166.000; Tempo pieno

Vantaggi offerti da questo lavoro

Health insurance

RSUs

401(k) matching

Descrizione del lavoro

Amazon is seeking an Applied Scientist II to advance ML and applied mathematics for large-scale optimization and orchestration in fulfillment networks. You will translate business problems into tractable scientific challenges and build ML systems, first-principles models, edge-deployable prototypes, and production-ready solutions.

You will collaborate across science and engineering teams to deploy your work, mentor junior scientists, and contribute to research publications while shaping the

Competenze

• PhD in engineering, technology, computer science, ML, robotics, OR, statistics, mathematics or equivalent quantitative field.
• 1+ years of building ML models for business applications.
• Experience programming in Java, C++, Python or related language.
• Strong foundation in applied mathematics, statistics, and ML across domains.

Mansioni

• Design, develop, and deploy ML and scientific solutions spanning ML, statistics, computer vision, optimization, and physics-informed modeling in production environments.
• Rapidly ramp on unfamiliar problem domains and prototype solutions end-to-end.
• Publish research findings for internal or external venues and mentor peers.
• Prototype and evaluate sensing hardware and edge-deployable models for real-world constraints.
• Collaborate with multiple teams to deploy solutions and guide junior scientists.

Conoscenze

Java

C++

Python

Applied mathematics

Statistics

ML frameworks

Edge deployment

Formazione

PhD in engineering, CS, ML, robotics, OR/stats or equivalent quantitative field

Strumenti

PyTorch

TensorFlow

NumPy

SciPy

Descrizione del lavoro

Amazon is seeking an Applied Scientist II to advance ML and applied mathematics for large-scale optimization and orchestration in fulfillment networks. You will translate business problems into tractable scientific challenges and build ML systems, first-principles models, edge-deployable prototypes, and production-ready solutions.

You will collaborate across science and engineering teams to deploy your work, mentor junior scientists, and contribute to research publications while shaping the