Data Scientist, Alexa Audio Data and Insights, Inc.
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  • Experience

    2-4 year
  • Salary

  • Location

    Austin, TX, USA
  • Job Function

    Data Scientist
  • Industry

    Information Technology
  • Qualification

    Degree in Computer Engineering, Degree in Finance, Degree in Computer Science, Degree in Mathematics, Degree in Statistics

Key Skills

Java Programming, Design, Python Programming, SQL, AWS, PowerBI, Big Data Technology, Machine learning techniques, Data science techniques, R Programming, Amazon RedShift, Amazon Elastic Compute Cloud-EC2, Amazon Simple Storage Service (S3), Large scale data processing, Data pipelines

Job Description

Amazon’s Alexa is a cloud-based voice service that powers Amazon’s groundbreaking voice-powered devices. These devices are part of Amazon’s vision to build a computer in the cloud that is completely controlled by your voice. Alexa Music is a critical Alexa domain, focused on delivering magical music experiences that drive adoption and engagement with Alexa.

The Alexa Audio Data and Insight (AUDI) team consists of 12 talented Data Scientists, Business Intelligence Engineers, and Data Engineers. We seek an experienced Senior Data Scientist to drive Alexa Music customer engagement. You are the data science lead to define the ambiguous space. We are looking for a thought leader and you demonstrate this by delivering solutions, not just by having ideas. We encourage you to shape the business strategy with data driven recommendations. A successful candidate has an entrepreneurial spirit and wants to make a big impact on Alexa Music customers. You will develop strong working relationships and thrive in a collaborative team environment. Your role requires the ability to influence a virtual team of contributors and interact with marketing executives. You draw from a broad data science expertise to mentor Scientists and Business Intelligence Engineers; following a rigorous scientific methodology, while providing leadership on complex technology issues. You provide guidance on cutting-edge methods in big data processing, data science literature, experimentation and careful consideration of modeling decisions. We expect you to have breadth of data science knowledge, familiar with casual inference and depth in predictive modeling (supervised learning) and A/B testing.


  • Develop predictive models and unsupervised learning to identify streamer resilience issues.
  • Drive scientific best practices across teams mentoring others based on learnings gained through Alexa Entertainment’s initiatives such as improving accuracy and reduce customer friction.
  • Proactively seek to identify business opportunities and provide solutions based on a broad and deep knowledge of Amazon’s data resources, industry best-practices, and work done by other teams.
  • Partner with, coordinate, and influence multiple teams outside of Alexa Entertainment (Alexa Finance, Alexa Experience Data, Amazon Music, etc.), to support key initiatives.
  • Be the voice of the customer (end customer and data consumer), aligning stakeholders with scalable mechanisms to incorporate our models into product and engineering decision-making processes

A day in the life

When we work together, we operate:

Centralize the sprint and consolidate to one queue but still give stakeholder visibility and continue to ask feedback;

  • Advocate knowledge sharing and code review;
  • Avoid single point of failure with a secondary owner as code reviewer, mentor, backup, etc;
  • Consolidate across org initiatives to the same primary owner;
  • Train everyone on the must-have knowledges;
  • Carve out time for high-sev tt, urgent requests, tech debt failure;
  • Favor problem statement other than data requests

About the team

We are a team of 13 made up of Business Intelligence Engineers (BIEs), Data Engineers (DEs), and Data Scientists (DS’s), who bring with deep experience in both business analytics and data science. We cover the data needs of Music, Radio, Podcast, Books, and the Audio Category.


  • Bachelor’s degree in Computer Science, MIS, Mathematics, Statistics, Finance, related technical field, or equivalent work experience
  • 4+ years of years of relevant work experience in data science and research or related field
  • Knowledge of causal inference, regression, classification, design of experiments
  • Expertise with large-scale, complex dataset processing and knowledge of model deployment
  • Both technically deep and business savvy enough to interface with all levels and disciplines within the organization
  • Efficiency in at least one scripting language such as R or Python, and in one query language such as SQL
  • Ability to solving loosely defined problems using data science methodologies


  • Graduate degree in computer science, business, mathematics, statistics, economics, or other quantitative field
  • Experience with large-scale data warehousing and BI solutions, including using AWS technologies – Redshift, S3, Spectrum, EC2, Data Pipeline and other big data technologies
  • One specific area of expertise (e.g. estimation and hypothesis testing, linear regression, casual inference, forecasting models, core fundamentals, etc.)
  • Demonstrated ability to coordinate projects across functional teams, including engineering, IT, product management, marketing, finance, and operations
  • 7+ years of hands-on experiences of classical machine learning methods.

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

Company Info., Inc., Inc. is an American multinational technology company with operations in cloud computing, streaming media, artificial intelligence, and e-commerce. The company has been referred to as one of the most influential economic and cultural forces in the world, and it is one of the world's most valuable brands.

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