Data Scientist II Amazon
Seattle, WA

Job Description

Are you passionate about transforming Amazon high volume hiring experience for hourly workforce? Are you a self-starter with experience leading innovation projects? Do you embrace any opportunity to drive business improvement with data and analytics? Do you enjoy questioning the status quo? Do complex and difficult challenges excite you? If yes, this may be the team for you.

Amazon's mission is to be the most customer centric company in the world. The Workforce Staffing organization is on the front line of that mission by hiring the hourly fulfillment associates who make that mission a reality. We are the research team that dedicated to the global hourly workforce in Amazon to drive the necessary growth and continued scale of Amazon's associate needs within a constrained employment environment. This program will re-invent how Amazon attracts, communicates with, and ultimately hires its hourly associates. This team will own multi-layered research and program implementation to drive deep learnings, process improvements, and strategic recommendations to global leadership.

This role will be responsible for challenging the status quo by analyzing and identifying factors that predict and forecast success and failure in Amazon's current and future labor markets. You will own the analysis across multiple data sources to build a health metric by market, predicting risk before it occurs. The ideal candidate will possess strong analytical and technical expertise to build analytic solutions to drive business improvements at scale.

Key Responsibilities

* Identify key market and company factors that introduce risk to meeting labor orders across our full time and part time hourly associate populations
* Evaluate effectiveness and forecast ROI on key decisions to increase Amazon's employment value proposition
* Build models that improve hiring funnel efficiency at scale
* Influence key business decisions in potentially adjusting labor plans, shift structures, compensation and/or benefits, and other factors to increase Amazon's appeal across a diverse workforce
* Build predictive models to anticipate labor order volumes and fluctuations before they occur