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Sr. Applied Scientist, Engine AI Center of Excellence (AICE)

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Amazon

5d ago

  • Job
    Full-time
    Senior & Expert Level
  • Data
    IT & Cybersecurity
  • Berlin
    Remote

AI generated summary

  • You must have a PhD in Computer Science, extensive ML experience, expertise in AI techniques, coding skills, and a track record in scalable systems and publications, plus leadership experience.
  • You will map business challenges to solutions, design machine learning models, lead collaborations, and develop end-to-end data processing pipelines in a fast-paced environment.

Requirements

  • PhD degree in Computer Science or related field
  • Several years experience building machine learning models for business applications using Python, Java, C++ or related language.
  • Documented expertise in machine learning/artificial intelligence: data processing, neural networks, deep learning, estimators, regression, information theory, optimization, statistical analysis, signal processing, graph mining, causality analysis.
  • Experience with patents or scientific publications at peer-reviewed conferences or journals and generally excellent writing and communication skills.
  • Experience in any of the following or similar areas: anomaly detection, time series analysis, LLM-agents, correlation analysis, causality modelling, graph modelling, probabilistic modelling, nlp, text mining.
  • Experience in data-driven and automated fault/incident management and service reliability systems at scale.
  • Experience with machine learning frameworks, distributed storage systems, or data processing frameworks, and data visualization tools.
  • Experience in designing and developing large, scalable production systems and architectures.
  • Project leader and/or team lead experience.

Responsibilities

  • As a Sr. Applied Scientist of the Engine AICE team, you have the important role of mapping business challenges to high-impact solutions in areas where the business problem or opportunity may not yet be defined. You turn theoretically sound methods into practically applicable models designed for processing massive volumes of data in large-scale environments. You define business relevant solutions implemented as end-to-end machine learning functions and data processing pipelines that integrate with our partners production systems. In a fast-paced innovation environment, you advise and work closely with our Applied Scientists, Machine Learning Engineers, Software Development Engineers, and partner teams to design machine learning models and experiments at scale. You are recognized for your expertise in all aspects of the practical machine learning development cycle, encompassing sound use of data pre-processing techniques, analysis, modelling, and validation methods. You take lead of the scientific and technical work in cross-team collaborations.

FAQs

What are the educational requirements for this position?

A PhD degree in Computer Science or a related field is required for this position.

What programming languages should I be proficient in for this role?

Proficiency in Python, Java, C++, or a related language is required for building machine learning models.

What specific areas of expertise are needed for this job?

Documented expertise in machine learning/artificial intelligence, including data processing, neural networks, deep learning, estimators, regression, information theory, optimization, statistical analysis, signal processing, graph mining, and causality analysis is required.

Is experience with patents or publications necessary?

Yes, experience with patents or scientific publications at peer-reviewed conferences or journals is necessary, along with excellent writing and communication skills.

What types of projects will I be working on?

You will be working on projects related to data-driven automation capabilities that support critical service operations in Retail and IT, including anomaly detection, time series analysis, classification, causal inference, and text mining.

Will I be working alone or as part of a team?

You will be working as part of a diverse team that includes Applied Scientists, Machine Learning Engineers, Software Development Engineers, and partner teams.

What are the key responsibilities of this position?

Key responsibilities include mapping business challenges to high-impact solutions, turning theoretical methods into practical applications, defining end-to-end machine learning functions, and collaborating across teams to design and evaluate machine learning models at scale.

Is there a focus on any specific technology or methodology?

Yes, there is a focus on applying the latest techniques in agentic workflows with Large Language Models (LLMs), probabilistic modeling, estimation, and deep neural networks.

What opportunities for growth are available in this role?

This role offers exciting challenges that will help you grow as an applied scientist and technical leader, combining scientific and engineering skills to solve complex machine learning problems.

What qualities does Amazon value in its employees?

Amazon values passion for discovery, invention, simplification, and building, as well as a diverse workforce that enhances the success of the company.

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Amazon

Retail & Consumer Goods
Industry
10,001+
Employees
1994
Founded Year

Mission & Purpose

Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. We are driven by the excitement of building technologies, inventing products, and providing services that change lives. We embrace new ways of doing things, make decisions quickly, and are not afraid to fail. We have the scope and capabilities of a large company, and the spirit and heart of a small one. Together, Amazonians research and develop new technologies from Amazon Web Services to Alexa on behalf of our customers: shoppers, sellers, content creators, and developers around the world. Our mission is to be Earth's most customer-centric company. Our actions, goals, projects, programs, and inventions begin and end with the customer top of mind. You'll also hear us say that at Amazon, it's always "Day 1."​ What do we mean? That our approach remains the same as it was on Amazon's very first day - to make smart, fast decisions, stay nimble, invent, and focus on delighting our customers.