Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? If so, the WW Amazon Logistics, Business Analytics team is for you. We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner.
We are looking for an enthusiastic, customer obsessed, Sr. Applied Scientist with good analytical skills to help manage projects and operations, implement scheduling solutions, improve metrics, and develop scalable processes and tools. The primary role of an Operations Research Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how the final phase of delivery is done at Amazon.
Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, and the ability to use data and research to make changes.
This role requires robust program management skills and research science skills in order to act on research outcomes. This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment.
Responsibilities may include:
- Develop input and assumptions based preexisting models to estimate the costs and savings opportunities associated with varying levels of network growth and operations
- Creating metrics to measure business performance, identify root causes and trends, and prescribe action plans
- Managing multiple projects simultaneously
- Working with technology teams and product managers to develop new tools and systems to support the growth of the business
- Communicating with and supporting various internal stakeholders and external audiences
Basic Qualifications
- 10+ years of building machine learning models or developing algorithms for business application experience
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 10+ years of industry or academic research experience
- Knowledge of programming languages such as C/C++, Python, Java or Perl
- Experience with neural deep learning methods and machine learning
Preferred Qualifications
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- 6+ years of post PhD experience experience
- Knowledge of deep learning, machine learning and statistics
- 4+ years of scripting, programming, or security code review in a common language, such as Python, Java or C++ experience
- Knowledge of mathematical/statistical/physics fundamentals
- Experience with algorithm and model development work for large-scale applications
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience in science or engineering team management
- Experience establishing successful partnerships with internal and external teams to execute tactical initiatives or equivalent
- Experience shaping business strategy for technical products or services for large enterprises or partners
- Experience in written and verbal communication skills to communicate with technical and non-technical audiences, including senior leadership
- 4+ years of data science, business analytics, business intelligence, or similar experience in big data environments experience
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
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