Senior Applied Scientist, Logistics Network, Japan Transportation BITES (Business Intelligence in Tecnology of Engineesing & Science)

Applied ScientistAlgorithm/HeuristicSenior Applied Scientist

E
Amazon JapanBusiness OperationAWSAmazon ToolEngineer

Join a rapidly growing organization and lead the transformation of logistics through your data science and algorithm skills.

The Transportation team at Amazon Japan is seeking a Senior Applied Scientist to spearhead the development of algorithmic/heuristic models and solutions to optimize and monitor our logistics network.

We operate Japans largest e-commerce marketplace with the mission of becoming Earths most customer-centric company. We are building a network that can deliver the products customers want when they want them as quickly as possible. Timely replenishment wide product selection and reliable delivery are the core of our service but the logistics and retail landscape is constantly evolving. Maintaining high-quality service amidst these changes is critical to our competitiveness.

As Amazon Japan grows the complexity of our supply chain continues to increase demanding greater efficiency visibility and improved decision-making in our business operations. We are looking for Engineers who can leverage AWS and Amazons proprietary tools to provide optimal models and solutions to address these challenges.

Key job responsibilities
Business Impact
NetworkScientistTechBizManager
()()Manager
Amazon



Provide model and solution to address complex high-business-impact challenges. Design from the architectural level and collaborate with other engineering teams as needed.
As the dedicated and first Scientist for the Network team you will boldly take on unprecedented initiatives and effectively engage with stakeholders. Work with both tech and business managers to establish a roadmap and deliver solutions that meet business needs. A strong learning appetite for the domain and ability to be a full-stack scientist/engineer are required.
Collaborate with managers to establish mechanisms that set network operation metrics (accuracy efficiency etc.) and operational standards (quality consistency reliability etc.). You will need to efficiently communicate analysis results to the user network team without relying on specialized terminology.
Strategic thinking and trade-off decisions are necessary. Your decisions will have significant impact on Amazons logistics network.
Ability to effectively communicate ideas both verbally and in writing to all types of stakeholders. You will produce content ranging from strategic documents to operational guides on using your developed tools across the stack.
Lead projects with limited guidance.

A day in the life
10:00 Lead
10:30 Data EngineerBeam Search Bandwith Scheduler
11:00 Notebook
12:00
13:30 Scrum Daily Meeting
14:00 Biz side
14:30
15:00
16:00 Bayes
18:00 Project WeeklyfigureNotebook
18:30 Approve
19:30

About the team
Algorithm / Data Science3
1: Data Mart
2: 1
3: 1Business InsightData PipelineDashboard

280%Business Impact
1

Our team provides Algorithm/Data Science solutions with the main focus on the following three areas:

Overhauling and maintaining a large-scale Data Mart to comprehensively manage and analyze long-haul transportation data.

Delivering optimization and machine learning-based algorithmic solutions utilizing the data from the above Data Mart.

Providing Data Pipeline and Dashboard solutions to derive business insights from the data in the Data Mart.

In particular the second area has seen growing demand and we are accelerating the development in this domain. For example this accounts for approximately 80% of new development needs by volume and the business impact is significantly greater compared to our previous projects.

The machine learning-based solutions we have started providing just over a year ago have become a rapidly growing and highly anticipated area.

- PhD or Masters degree and 6 years of applied research experience
- Experience programming in Java C Python or related language

- Experience in building machine learning models for business application
- Experience with neural deep learning methods and machine learning
- Experience with large scale distributed systems such as Hadoop Spark etc.
- Experience with modeling tools such as R scikit-learn Spark MLLib MxNet Tensorflow numpy scipy etc.

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