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Applied Scientist

2 mesi fa

Milano, Lombardia, Italia AWS EMEA SARL (Italy Branch) - G99 A tempo pieno
Role Overview

Machine learning (ML) has been a cornerstone of Amazon's strategy since its inception. We have established ourselves as leaders in various domains including recommendation systems, product search, eCommerce fraud detection, and the large-scale optimization of fulfillment operations.

The Generative AI team is dedicated to assisting AWS clients in harnessing Generative AI to address business and operational challenges, fostering innovation within their organizations.

As an applied scientist, you will excel in the design and development of sophisticated ML models aimed at tackling a wide array of challenges and opportunities. You will engage with extensive datasets comprising text, images, and other data types to address real-world issues. Your responsibilities will include designing and executing experiments, researching novel algorithms, and discovering innovative methods to enhance risk management, profitability, and customer satisfaction.

Key Responsibilities
  • Develop, assess, and innovate ML models to address diverse challenges across various sectors.
  • Engage directly with clients to comprehend their business needs and assist in defining and implementing scalable Generative AI solutions.
  • Collaborate closely with account teams, research scientists, and product engineering teams to facilitate model implementations and develop new solutions.
About the Team

The team is committed to helping clients envision and define use cases that will yield the highest value for their businesses. We guide them in selecting and refining appropriate models, navigating technical or business obstacles, developing proof-of-concepts, and planning for large-scale solution deployment.

The GenAI Innovation Center team offers insights on best practices for the responsible and cost-effective application of generative AI.

Diversity and Inclusion

Amazon values diverse experiences. We encourage candidates from all backgrounds to apply, even if they do not meet every preferred qualification.

Why AWS?

Amazon Web Services (AWS) stands as the most comprehensive and widely adopted cloud platform globally. We pioneered cloud computing and continue to innovate, which is why clients ranging from successful startups to Global 500 companies rely on our extensive suite of products and services.

Work-Life Balance

We prioritize work-life harmony. Achieving success at work should not compromise personal life, which is why flexible work hours and arrangements are integral to our culture.

Inclusive Team Culture

At AWS, we foster a culture of learning and curiosity. Our employee-led affinity groups promote inclusion and empower us to celebrate our differences.

Mentorship and Career Development

We are committed to continuous improvement and strive to be Earth's Best Employer. You will find abundant opportunities for knowledge-sharing, mentorship, and career advancement resources to help you grow as a well-rounded professional.

Basic Qualifications
  • Several years of experience in building models for business applications.
  • PhD or Master's degree with several years of experience in Computer Science, Computer Engineering, Machine Learning, or a related field.
  • Experience with patents or publications in top-tier peer-reviewed conferences or journals.
  • Proficiency in programming languages such as Java, C++, Python, or similar.
  • Knowledge in algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, and high-performance computing.
  • Experience with neural deep learning methods and machine learning.
Preferred Qualifications
  • Experience in professional software development.
  • PhD in computer science, engineering, mathematics, operations research, or a highly quantitative field.
  • Practical experience in resolving complex problems in an applied setting.
  • Hands-on experience with deep learning frameworks like MXNet, TensorFlow, or PyTorch.
  • Strong communication skills with attention to detail, capable of conveying complex mathematical concepts to non-experts.
  • Able to thrive in a fast-paced, collaborative, and dynamic work environment.

Scientific thinking and the ability to innovate, along with a proven track record of thought leadership and contributions that have advanced the field, are essential.