Data Scientist, Alexa Smart Home

Alexa Smart Home is re-imagining how customers interact with and control their smart home devices. We process hundreds of millions of customer actions weekly across diverse device types manufacturers and APIs and were looking for a Data Scientist II to help us drive customer experience improvements uncover insights and build intelligent AI/ML-powered solutions at massive scale.
As a Data Scientist on the Alexa Smart Home team you will develop machine learning models analytical frameworks and Gen-AI powered solutions that improve product quality inform strategic decisions and enable proactive detection of customer experience issues across the smart home ecosystem. You will work with large-scale behavioral and interaction datasets design experimentation frameworks and partner with engineering product and science teams to deliver data-driven solutions that directly impact millions of Alexa customers worldwide.
This role offers the opportunity to work on cutting-edge problems from building AI/ML solutions that predict and prevent customer-facing issues to designing analytics systems that surface actionable insights for improving smart home reliability.

Key job responsibilities
Design and implement AI/ML models anomaly detection systems and statistical frameworks to analyze customer interactions identify emerging issues and drive measurable improvements across the smart home ecosystem.
Own the full lifecycle of model development from exploratory analysis and feature engineering through deployment monitoring and continuous improvement.
Partner with product engineering and science teams to translate complex business and technical problems into scalable production-ready data science solutions.
Design and run rigorous experiments (A/B testing causal analysis) to measure the impact of product and quality improvements and guide strategic decisions.
Develop scalable analytical solutions for impact measurement KPI development metric integrity validation and long-term business monitoring.
Explore and apply GenAI and LLM-based approaches to accelerate insight generation automate analytical workflows and solve novel smart home challenges.
Communicate findings and recommendations to senior leadership through clear data narratives written documents and presentations that influence product and business strategy.
Collaborate with colleagues across science and engineering disciplines for fast turnaround proof-of-concept prototyping at scale.
Contribute to the broader science community by mentoring analysts improving data workflows and tooling and sharing technical work in internal and external forums.


A day in the life
Deep dive into smart home interaction metrics analyzing trends failure categories and model performance across dashboards and datasets.
Developing code: building packages in Python writing SQL queries and deploying ML solutions that Smart Home teams consume.
Leading or joining working sessions with Product Managers to refine problem statements for new initiatives.
Exploring new features and model architectures leveraging AWS services and LLMs to solve smart home-specific challenges.
Partnering with engineers to align on solution designs and ensure data science insights translate into robust production-ready systems.
Owning or co-owning WBR/MBR documents reviewed with Smart Home leadership.

About the team
The Alexa Smart Home Decision Sciences team provides the data and analytical foundation that powers insights across our rapidly growing smart home ecosystem. We work with data from millions of connected devices customer interactions and partner integrations to help shape product strategy improve customer experience and drive business growth. We operate in a collaborative environment where innovation and customer obsession are at the core of everything we do.

- 3 years of data scientist experience
- 3 years of data querying languages (e.g. SQL) scripting languages (e.g. Python) or statistical/mathematical software (e.g. R SAS Matlab etc.) experience
- 3 years of machine learning/statistical modeling data analysis tools and techniques and parameters that affect their performance experience
- 1 years of working with or evaluating AI systems experience

- Ph.D. in Science Technology Engineering or Mathematics (STEM)
- Knowledge of machine learning concepts and their application to reasoning and problem-solving
- Experience in defining and creating benchmarks for assessing GenAI model performance
- Experience working on multi-team cross-disciplinary projects
- Experience applying quantitative analysis to solve business problems and making data-driven business decisions
- Experience developing experimental and analytic plans for data modeling processes use of strong baselines ability to accurately determine cause and effect relations

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