Principal Business Intel Amazon Seattle

Job Description: • 2-minute read •
Amazon Fulfillment Services is looking for a motivated individual with strong analytical skills and practical experience to join our Modeling and Optimization team to manage the predictive analytics group. The group will build statistical models, apply machine learning techniques, analyze very large data sets, and construct metrics using these modeling techniques to drive business improvement. The predictive analytics group will work closely with other groups within the modeling and optimization team to leverage the overall team’s optimization expertise and to guide the development of visualization tools that the group will employ.
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Because we are driven by faster delivery to customers, a more efficient supply chain network, and lower cost of operations, our main focus is in the development of analytical modeling and visualization tools fed by our massive amounts of available data. You will be responsible for building models and managing the team that is building these analytical tools that improve the economics of the North American, European, and Japanese fulfillment networks as amazon increases the speed and decreases the cost to deliver product to customers. You will identify and evaluate opportunities to reduce variable costs by improving the fulfillment network topology, inventory placement, transportation operations and scheduling, fulfillment center processes, and the execution to operational plans. You will also improve the efficiency of capital investment by helping the fulfillment centers to improve storage utilization and the effective use of automation. Finally, you will help create the metrics to quantify improvements to the fulfillment costs (e.g., transportation and labor costs) resulting from the application of these optimization models and tools.
Some of the relevant problems include constructing predictive models of transportation cost to identify changes to network structure and customer behavior, evaluating transportation on-time performance and identifying root causes of failures, identifying drivers of fulfillment center costs and cycle time, defining the value of a given inventory state, measuring the accuracy of our execution to plan and predictions of system capabilities.

These analyses and models will guide the business decisions by highlighting opportunities, identifying correlations, defining experiments, and determining cause and effect relationships. The team will consist of research scientists and business intelligence engineers. The manager will leverage the expertise of each individual to construct models, perform analyses, and derive relevant metrics. The manager must have relevant domain knowledge to teach and mentor less experienced group members and to critique models and approaches taken by the group in terms of both business relevance, technical validity, and computational performance.
Basic Qualifications

Demonstrated experience leading and building an analytics team for five years or more.
Ability to manage and quantify improvement in an area of the company’s business resulting from the application of business analytics, statistical models, optimization techniques, and machine learning.
Expertise in SAS and/or R and SQL. Application of these tools to build statistical models and to lead teams of data engineer, business analysts, and research scientists building models.
Application of these modeling techniques on large sets of data.
Demonstrated use of modeling results to improve business decisions, particularly using model-based decision criteria in real time systems.
Preferred Qualifications

Experience with forecasting and time series analysis.
Experience with risk analysis for both transactional process and long term decisions.
Good communication skills with both technical and business people. Ability to speak at a level appropriate for the audience.
Experience writing scripts (Perl, Ruby, Groovy) to manipulate data and developing software in traditional programming languages (C++, Java, Clojure, Python).
Experience implementing machine learning algorithms, tailored to particular business needs and tested on large datasets.
Familiarity in GIS, geocoding and computational geometry.
Familiarity with data visualization (e.g. R, ggplot, ggobi, Tableau).Familiarity designing simulation and optimization models in for business decisions (e.g., staff scheduling, vehicle routing, and facility location) and/or feedback and/or predictive control.
Ph.D. in Statistics, Applied Mathematics, Computer Science, Operations Research, or a related field with publications in refereed academic journals.

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