Applied Scientist
il y a 1 jour
Experience programming in Java, C++, Python or related language
- Master's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field
- Experience developing machine learning models for natural language processing
The team automates the classification process through technical solutions that rely heavily on machine learning operations and underlying machine learning models. We partner closely with business, operations and tech teams worldwide to deliver complex, ambitious roadmaps with the customer's goal in mind. We leverage off the sciences of data, information processing, machine learning, and generative AI to improve user experience, automation, service resilience, and operational efficiency. Our work expands AI-driven capabilities of our Retail businesses.
We are looking for a successful an Applied Scientist (AS) to help develop a general framework for classification and related tools to help improve the accuracy of the algorithms. The AS will work with stakeholders from multiple business units to gather requirements and to build multi-modal ML models for classification.
As an applied scientist, you will invent, build and deploy state of the art machine-learning models and systems to enable and enhance the team's mission—real world incarnations of some of the most difficult academic problems.
He/she will work closely with the business and technical teams to analyze many non-standard and unique business problems and use creative problem solving to deliver results. He/she will be a self-starter, comfortable with ambiguity, able to think big and be creative while paying careful attention to detail and will enjoy working in a fast-paced and dynamic environment.
Key job responsibilities
- Work on research projects in machine learning and related fields to create highly innovative customer experiences;
- Write highly optimised production-ready code to maximize throughput and latency for high volume traffic.
- Analyze large amounts of data to discover patterns, find opportunities, and develop highly innovative, scalable algorithms to seize these opportunities;
- Validate models via statistically rigorous experiments across millions of customers;
- Work closely with software engineering teams to build scalable prototypes for testing, and integrate successful models and algorithms in production systems at very large scale;
- Use machine learning and analytical techniques to create scalable solutions for business problems.
- Analyze and extract relevant information from large amounts of historical business data to help automate and optimize key processes.
- Design, development, and evaluation of highly innovative models for predictive learning.
- Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation, and model implementation.
- Research and implement novel machine learning and statistical approaches.
- Work closely with data & software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale.
A day in the life
We are a group that has fun at work while driving incredible customer, business, and environmental impact. We are backed by a strong leadership group dedicated to operational excellence that empowers a reasonable work-life balance. As an established, experienced team, we offer the scope and support needed for substantial career growth.
About the team
We work back to back to address the technical challenges of automation across a variety of products, software, and systems. Our scientists and machine learning engineers work in synergy to solve hard problems and enrich each other's skills. Together, we are a powerful team of worldwide specialists in bringing the potential of practical ML and AI to the max with impact on millions of Amazon customers.
Luxembourg, LUX
- Experience implementing algorithms using both toolkits and self-developed code
- Have publications at top-tier peer-reviewed conferences or journals
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