Job Description

DESCRIPTION

Want to work on one of Amazons most ambitious efforts? Time and Attendance (TAA) is leading the charge to build products that support our global workforce of passionate Amazonians! Oh, and we need to accurately track billions of hours per year to ensure those employees are paid accurately.

TAA is looking for a strong Data Science to inform science and data strategies that enable us to deliver best-in-class Time and Attendance systems and products to our customers, worldwide. This Data Scientist will own and drive a greenfield and ambiguous space -- how do we leverage advanced analytics and machine learning to enable seamless time and attendance experiences for managers and employees alike? For example, can an associate's clock in and out patterns enable us to automate their time capture so they no longer need to record their working time? Can we identify scheduling and attendance trends through deep data mining and analysis to optimize the way we schedule employees across our businesses? This Data Scientist will be tasked with defining and solving these problems, with full autonomy to prioritize the opportunities that drive the largest impact for customers.

Time and Attendance (TAA) is a rapidly growing team tackling new, hard problems that Amazon has not solved at scale and creating fundamentally improved ways for employees to record time and receive accurate pay. Our customers include each and every Amazon employee globally... we're talking millions of salaried employees and hourly workers on every continent...those who work in corporate offices, fulfillment warehouses, customer service sites, Alexa data services, AWS data centers, physical retail stores like Amazon Books and Go, subsidiaries, and these days, even home offices! Factor in the number of different variations of a work week, work day, overtime, and an equally daunting amount of reporting requirements. Account for rapid and dynamic way our businesses evolve and operate in order to drive innovation for both employees and customers -- for example, the ability for hourly workers to complete all training virtually, to work across Amazon businesses, and even to work across locations. We must be able to move at Amazon speed and continuously innovate to delight our customer. Are you up for the challenge?

The ideal candidate isan experienced and motivated Data Scientist (DS) with outstanding leadership skills, proven ability to build and manage medium-scale modeling projects, identify data requirements, and build methodology and tools that are statistically grounded. The candidate will be an expert in the areas of data science, machine learning and statistics, and is comfortable facilitating ideation and working from concept through to execution. The successful Data Scientist will have the extreme bias for action needed in a startup environment.
BASIC QUALIFICATIONS

Bachelor's Degree
3+ years of experience with data scripting languages (e.g SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
2 years working as a Data Scientist
Experience with statistical analysis, research science, machine learning and deep learning techniques, data modeling, regression modeling, financial analysis, and demand modeling.
Excellent written and verbal communication skills. Strong ability to interact, communicate, present, and influence within multiple levels of the organization.
Proficiency with QuickSight/Tableau/R Shiny/Dash or other web-based interfaces to create graphic-rich customizable plots, charts data maps etc.

PREFERRED QUALIFICATIONS

Previous experience in a ML or data scientist role with a large technology company.
Experience in an operational environment developing, fast-prototyping, piloting and launching analytic products.
Experience in writing academic-styled papers for presenting both the methodologies used and results for data science projects.
Experience with AWS services (Redshift, S3, EC2, Lambda, SNS/SQS, RDS, Aurora) preferred.
Ability to distill informal customer requirements into problem definitions, quantify improvement in customer experience or value for the business resulting from research outcomes, dealing with ambiguity and competing objectives.
Experience with applying Machine Learning and Data Science in HR, Marketing, Advertising, or the social and behavioral sciences.