Senior Applied Scientist - Global Operational Excellence

Disrupting the way Amazon fulfills our customers' orders.

Amazon operations is changing the way we improve Customer Experience through flawless fulfillment focused on 1) successful on-time delivery, 2) at speed and 3) at the lowest possible cost.

Being the engine of Amazon Operational excellence, driving zero defects through ideal operation, being the heart of the Fulfillment network and its center of excellence, being proactive and aspiring for zero defects across the network with 100% organizational engagement.

For example, our applied science team leverage a variety of advanced machine learning and cloud computing techniques to power Amazon's operations performance management.

This includes building algorithms and cloud services using LLMs, deep neural networks, and other ML approaches to make root cause analysis of incidents and defects better.

They develop machine learning models to predict inbound capacity forecasts and select the optimal order of unloading and stowing the incoming items in the Fulfilment Center.

The teams also utilize Langchain, Amazon Bedrock, Amazon Textract, ElasticCache Redis, Opensearch and Kubernetes to extract insights from big data and deliver recommendations to operations managers, continuously improving through offline analysis and impact evaluation.

Underpinning these efforts are unique technical challenges, such as operating at unprecedented scale (100k requests per second with SNS / SQS and

About the team

GOX DEA team is the engine of Amazon Operational excellence at the heart of the fulfillment network operations, aspiring zero defects.

It is our purpose to improve Customer Experience through flawless fulfillment focused on 1) successful on-time delivery, 2) at speed and 3) at the lowest possible cost.

Our Solutions support on-time delivery of billions of packages to our customers across the globe leveraging AI & Generative AI technology.

Job ID : 2673046 Amazon EU Sarl

GOX team is looking for a Senior Applied Scientist to support our vision of giving our customers the best fulfillment experience in the world, and our mission of delighting our customers by providing capabilities, tools and mechanisms to fulfillment operations.

As Skynet Sr. APSCI, you would be providing resources and expertise for all data related reports (dashboard, scorecards ), analysis (statistical approach), and Machine Learning products and tools development.

On top of your internal customers within GOX team you would be supporting more widely with your experience and skills all across the org, partnering with a wide range of departments within Ops Integration (Packaging, Sustainability) within the company mainly with ICQA, ATS, AMZL, GTS on several projects.

You will be part of the community of Scientists within Amazon Operations including other AS, BIEs, SDEs, split across the different departments.

You will be part of projects requiring your close collaboration and interactions with Operations that require you to have a good understanding of product flow and process all along the distribution chain.

The GOX team is now recognized for its expertise and excellence in creating tools that improve massively the customer experience.

Several of them now rolled out in other regions with some of these tools becoming worldwide standard.

Reporting to the GOX Senior Manager, you will be responsible for developing the data-driven decision process from historical data and ML based predictive analysis and maintaining accurate and reliable data infrastructure.

You will work across the entire business, and be exposed to a wide range of functions from Operations, Finance, Technology, and Change management.

The successful candidate will be able to work with minimal instruction and oversight, manage multiple tasks and support projects simultaneously.

Maintaining your relationships with the customers in operations and within the team, while owning deliverables end-to-end is expected.

Critical to the success of this role is your ability to work with big data, develop insightful analysis, communicate findings in a clear and compelling way and work effectively as part of the team, raising the bar and insisting on high standards.

• Highly technical and analytical, possessing seven or more years of Machine Learning and / or Analytics Systems development and deployment experience, IT systems and engineering experience, security and compliance experience, etc.
• Possess significant experience of software development and / or IT and implementation / consulting experience.
• Strong verbal and written communications skills are a must, as well as the ability to work effectively across internal and external organisations and virtual teams.
• Ability to understand complex business requirements and render them as prototype systems with quick turnaround time.
• Experience with implementation and tuning in the Big Data Ecosystem, (such as EMR, Hadoop, Spark, R, Presto, Hive), ML Platforms (SageMaker, Kubeflow, Azure Machine Learning, SAS, Domino), and MLOps (model development, orchestration and deployment, monitoring, optimisation).
• Track record of implementing AWS services in a variety of business environments such as large enterprises and start-ups.
• Knowledge of foundation infrastructure requirements such as Networking, Storage, and Hardware Optimisation.
• BS level technical degree required; Computer Science or Mathematics background preferred. DB1
• AWS Certification, eg. AWS Solutions Architect, Developer, or AWS Certified Machine Learning - Specialty

Preferred qualifications

• Hands on experience leading large-scale big data and analytics projects.
• Hands on experience as a database, data warehouse, big data / analytics developer or administrator, or work as a data scientist.
• Hands on experience architecting, deploying and maintaining production machine learning systems.
• Demonstrated industry leadership in the fields of Big Data processing, Data Sciences and Machine Learning.
• Deep understanding of data, application, server, and network security
• Experience with Statistics, Machine Learning and Predictive Modelling.
• Working knowledge of modern software development practices and technologies such as agile methodologies and DevOps / MLOps.

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