• Data Scientist, Amazon Alexa AI

    Location US-WA-Seattle
    Posted Date 6 days ago(12/5/2018 8:17 PM)
    Job ID
    593484
    Company
    Amazon.com Services, Inc.
    Position Category
    Data Science
    Company/Location (search) : Country (Full Name)
    United States
  • Job Description

    Alexa is the groundbreaking cloud-based intelligent agent that powers Echo and other devices designed around your voice. Our team is creating the science and technology behind Alexa. We’re working hard, having fun, and making history. Come join our team! You will have an enormous opportunity to impact the customer experience, design, architecture, and implementation of a cutting edge product used every day by people you know.

    We’re looking for a passionate, talented and experienced Data Scientist to help make data-driven improvements to our existing models, in order to provide the best-possible experience for our customers. As a part of the Alexa AI team, you will be tasked with designing and implementing new online and offline metrics, to model and measure satisfaction and dissatisfaction (SAT/DSAT) for user-Alexa interactions on different end-points and device form factors. Additional responsibilities include:

    You Will Be Expected To:
    • Work with large amounts of real-world conversational data · Analyze, understand and model user-behavior and user-experience based on large scale data to detect key factors causing satisfaction and dissatisfaction (SAT/DSAT)
    • Ensure data quality throughout all stages of acquisition and processing, including data collection, ground truth generation, normalization and transformation
    • Build and measure novel online & offline metrics for personal digital assistants and search engines on diverse set of devices environments
    • Perform data quality analysis and build predictive models
    • Build and release models that elevate the customer experience and track impact over time
    • Define ML based techniques for sampling, judgment monitoring, spam detection, A/B testing and efficient evaluations
    • Work with the feature teams to define the online and offline end-to-end metrics for the product north star and guide them to optimize their online experiments to achieve those goals
    • Provide guidance and tools to the partner teams to understand the metrics and identify areas of improvements in their features
    • Full understanding of the system stack, each component, dependencies and constraints
    • Collaborate with colleagues from science, engineering and business backgrounds
    • Present proposals and results in a clear manner backed by data and coupled with actionable conclusions

    Basic Qualifications

    • Bachelors or Masters in a relevant field (Computer Science, Computer or Electrical Engineering, Mathematics, Physics, Statistics or a related field)
    • Experience in Python, Perl, or another scripting language

    Preferred Qualifications

    • PhD in a relevant field (Computer Science, Computer or Electrical Engineering, Mathematics, Physics, Statistics or a related field)
    • 3-5 years experience with various machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
    • Track record of diving into data to discover hidden patterns and of conducting error/deviation analysis
    • Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations
    • Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.
    • Ability to think creatively and solve problems
    • Strong attention to detail
    • Exceptional level of organization
    • Comfortable working in a fast paced, highly collaborative, dynamic work environment
    • Passion for data and numbers
    Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation
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