Description

　　Want to work for a fast-paced, innovative team? Want to work on ground-breaking initiatives? Want to work on problems that have massive scale but also need high precision? We are seeking a strong data science leader for our Workforce Staffing organization.

　　Workforce Staffing is responsible for hiring hourly associates into our global fulfillment operation. Each year we hire over 1 million associates across the globe. Workforce Intelligence (WFI), a subsidiary of Workforce Staffing (WFS), is responsible for driving decisions that help Workforce Staffing deliver the scale and precision it needs while minimizing the cost of hiring. WFI manages data acquisition, engineering, research, science and products that help WFS make the best decisions. Hiring over 1 million associates around the world presents the largest staffing challenge in a private company environment. The complexity is high and precision is needed because over hiring leads to unnecessary increase in wage and under hiring leads to delayed delivery of products to Amazon’s customers. There are over a dozen levers that WFS can pull to manage the scale and precision of hiring.

　　The team is composed of engineers, scientists, product and program management professionals. This role will oversee the work of 25+ scientists on the team without direct people management responsibilities. The scientists are distributed across domains like candidate attraction, candidate conversion, candidate intelligence, market intelligence et cetera. They build dozens of models which either drive automated decision making or deliver insights that inform human driven decisions that require science and judgement. This role will provide oversight for all the models within WFI, direct the approach for the most complex problems at hand and uphold high standards for how science work is performed in the organization.

　　The ideal candidate is a self-starter who is comfortable upholding high standards across a wide range of domains, can manage responsibility for large scale business impact and can communicate effectively with executive audiences. They are comfortable working with other engineering teams to architect their required data and infrastructure environments as well as with non-tech audiences to quantify business impact and establish roadmaps.

　　Key job responsibilities

　　You will be the science advisor to the head of WFI, driving overall strategy for the WFI science team. You will influence strategic decisions around which domains WFI should invest science resources in, and how to augment the science capabilities on the team through development and hiring.

　　On a day-to-day basis you will be responsible for making sure that the scientists on the team are solving the right problems, using the right techniques for the problem at hand, drawing the right conclusions and communicating appropriately to both the tech and non-tech stakeholders. You will lead a science panel facilitating this oversight of the data scientists, research scientists and economists on the WFI science team. Every science model goes through science panel review before getting deployed. You will raise the standards of science development and delivery. You will improve the review mechanism to deliver high quality outcomes while minimizing overhead.

　　You will take the lead in solving the most challenging science problems on the roadmap. You will be technically fearless and with a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance).

　　We are open to hiring candidates to work out of one of the following locations:

　　Arlington, VA, USA | Seattle, WA, USA

　　Basic Qualifications

　　MS in Mathematics, Statistics, Machine Learning, or a related quantitative field

　　10+ years industry experience working on data science and machine learning projects

　　Deep understanding and practical experience in several of the following areas: machine learning, statistics, optimization and forecasting

　　Demonstrated problem-solving skills with the ability to apply algorithms, which may include data profiling, clustering, anomaly detection, and predictive modeling methodologies

　　Preferred Qualifications

　　PhD in Mathematics, Statistics, Machine Learning, or a related quantitative field

　　12+ years of experience working in data science

　　Significant peer reviewed scientific contributions in Data Science, Analytics, Statistics, Optimization or related field.

　　Extensive experience applying theoretical models in an applied environment

　　Strong fundamentals in problem solving and algorithm design

　　Ability to uphold a high bar for science development across multiple domains

　　Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

　　Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $145,800/year in our lowest geographic market up to $294,700/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. Applicants should apply via our internal or external career site.