Data Science, Simulation


Job Overview


Amazon created one of the most sophisticated supply chains in the world. From the introduction of Amazon Prime, to the use of advanced technology for package delivery, Amazon consistently drives change from the front of the pack.
Amazon is seeking a detail oriented Data Scientist to focus on simulation to assist with process improvement and facility design initiatives in our North American fulfillment network. Successful candidates will be natural self-starters who have the drive to design, model, and simulate new fulfillment center conception and design processes.
The Data Scientist will be expected to deep dive problems and drive relentlessly towards creative solutions. This individual needs to be comfortable interfacing and driving various functional teams and individuals at all levels of the organization in order to be successful. Perform data modelling using different discrete event simulation software’s, and use optimization techniques, research aptitude, and data analysis such as statistical analysis, regression analysis, Design of Experiments (DOE) etc. to drive decisions on process and designs.
This role requires a strong passion for customers, a high level of comfort navigating ambiguity, and a keen sense of ownership and drive to deliver results. The ideal candidate will have experience in Science work, business analytics and have the aptitude to incorporate new approaches and methodologies while dealing with ambiguities in sourcing processes. Excellent business and communication skills are a must to develop and define key business questions and to build data sets. You should have demonstrated ability to think strategically and analytically about business, product, and technical challenges.
You must be responsive, flexible, and able to succeed within an open collaborative environment. Amazon’s culture encourages innovation and expects to take a high-level of ownership in solving complex problems.
Come help us make history!
· Design, develop, and simulate engineering solutions for complex material handling challenges considering human/equipment interactions for the North America fulfillment network
· Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance.
· Analyze historical data to identify trends and support decision making.
· Provide requirements to develop analytic capabilities, platforms, and pipelines.
· Design, size, and analyze field experiments at scale.
· Build decision-making models and propose solution for the business problem you defined. This may include delivery of algorithms to be used in production systems.
· Conduct written and verbal presentation to share insights and recommendations to audiences of varying levels of technical sophistication.
· Utilize code (python or another object oriented language) for data analyzing and modeling algorithm
· Develop, document and update simulation standards, including standard results reports
· Create basic to highly advanced models and run “what-if” scenarios to help drive to optimal solutions
· Analyze historical data to identify trends and support decision making.
· Apply statistical or machine learning knowledge to specific business problems and data.
· Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters.
· Provide requirements to develop analytic capabilities, platforms, and pipeline
· Ability to travel up to 10%


· Master’s degree in a highly quantitative field (Industrial Engineering, Operations Research, Machine Learning, Applied Mathematics, Statistics) or equivalent experience.
· 2+ years of experience developing quantitative models to solve complex problems.
· Expertise in at least one of the following areas: Discrete Event Simulation, Linear Programming, Natural Language Processing and Computer Vision.
· Strong coding skills in C++, Python or any other scripting languages
· Experienced in handling large data sets using SQL/Python in a business environment.


· PhD/Master’s degree in Statistics, Industrial Engineering/Operations Research, Mathematics or Statistics or related quantitative field with 5+ years of work experience.
· Experienced in writing academic-styled papers for presenting both the methodologies used and results for data science projects.
· Hypothesis testing (including statistical hypothesis testing) and experimental design.
· Experience with unstructured textual data
· Excellent verbal and written communication skills with the ability to effectively advocate technical solutions to research scientists, engineering teams and business audiences

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