Senior Applied Scientist, Last Mile Analytics

Amazon Inc.; Asti; Ibrido; EUR 120.000 - 180.000; Tempo pieno

Descrizione del lavoro

Amazon is seeking a Sr. Applied Scientist for Last Mile Science to lead ML model development and data-driven decision making across the物流 network. The role emphasizes building scalable models and tools to optimize delivery performance, costs, and service quality.

You will work cross-functionally with engineering, product, and operations teams in a fast-paced environment. The ideal candidate has a PhD or equivalent experience with strong analytics, ability to operate independently in ambiguous

Competenze

• At least 5+ years building ML models for business applications or related experience with strong research background.
• PhD in engineering, technology, computer science, ML, robotics, OR, statistics or equivalent; or Master's with 5+ years ML modeling experience.
• Proficiency in programming languages such as C/C++, Python, Java or Perl.
• Experience with neural networks and modern deep learning approaches.

Mansioni

• Develop input and assumptions for preexisting models to estimate costs and savings from network growth and operations.
• Create metrics to measure performance, identify root causes, trends, and prescribe actions.
• Manage multiple projects concurrently with robust program management.
• Collaborate with technology teams and product managers to build new tools and systems.
• Communicate insights to internal stakeholders and external audiences.

Conoscenze

ML model development

Deep learning

Data analysis

Formazione

PhD in Engineering/CS/OR

Master's +5 years ML experience

Strumenti

C/C++

Python

Java

Perl

Descrizione del lavoro

Amazon is seeking a Sr. Applied Scientist for Last Mile Science to lead ML model development and data-driven decision making across the物流 network. The role emphasizes building scalable models and tools to optimize delivery performance, costs, and service quality.

You will work cross-functionally with engineering, product, and operations teams in a fast-paced environment. The ideal candidate has a PhD or equivalent experience with strong analytics, ability to operate independently in ambiguous