Operations Research Scientist

Job description

DESCRIPTION

Workforce Staffing (WFS) is responsible for filling all of our First Mile, Middle Mile, and Last Mile Operations with hourly labor. In 2019 WFS will hire roughly 600K hourly associates across NA and EU and will receive over 5M job applications for employment. This role is part of the WFS Research & Business Intelligence (RBI) Team, tasked acquiring, modeling and visualizing all the data required to report out on performance metrics such as fill rates and funnel statistics, and forecasting hiring volumes to predict hiring risks and to support internal capacity planning.

As part of the WFS RBI team, the Ops Research Scientist will work on forecasting and optimization projects to improve funnel efficiency. The Ops Research Scientist will partner with capacity planning leaders, product/program managers, data engineers and other research scientists to build tools with clear business impact in an exciting and fast-paced start-up environment. The OR Scientist will be responsible for developing new predictive and optimization models as well as algorithms for key applications. The OR Scientist will be expected to be a thought leader as we chart new courses with our last mile planning models. Successful candidates will have a deep knowledge of computational optimization methods and mathematical modeling, background in statistical and machine learning methods, the ability to map models into production-worthy code, the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers, and the excitement to take iterative approaches to tackle big, long term problems.
Key Responsibilities:
· Guide the technical approach for the design and implementation of successful models and algorithms in support of teams across Amazon.
· Work in expert cross-functional teams delivering on demanding projects.
· Functionally decompose complex problems into simple, straight-forward solutions.
· Share knowledge in state-of-the-art statistics and machine learning research or frontier applied mathematical modelling and computation applicable to our problem space.

Desired profile

BASIC QUALIFICATIONS

· PhD in Operations Research, Industrial Engineering, Management Science or a related quantitative field and 5+ years of industry experience, or a Master's degree in the related fields with 5+ years of industry experience
· 5+ years experience building, iterating and validating statistical models
· Strong background in stochastic and deterministic optimization techniques and simulation
· Track record of building end to end modeling processes and workflows
· A working knowledge of linear and non-linear optimization methods accompanied by expertise in the use of OR tools (e.g. CPLEX, Gurobi, XPRESS).
· Practical experience in implementing optimization models and tools through the use of high-level modeling languages (e.g. AMPL, Mosel, R, MATLAB).
· Fluency in a high-level modeling language such as R, SAS, MATLAB or other statistical software
· Knowledge of relational databases (SQL)
· Experience in one or more major programming languages (C++, Java, Python, etc.)
· Experience building, iterating, and validating predictive data models from scratch
· Ability to convey rigorous mathematical concepts and considerations to non-experts
· Ability to distill problem definitions, models, and constraints from informal business requirements; and to deal with ambiguity and competing objectives
· Strong verbal and written communication, influencing and partnership skills