ADCI
Machine Learning Science, Science, alexa and amazon devices
DataScientistII
Neural analysis suggests this role is
optimal for Mid candidates.
“Data Scientist II at ADCI. Skills: Machine learning models, AI/ML-powered solutions, Analytical frameworks, GenAI, LLM. Design AI/ML models. Implement AI/ML models”
What You'll Achieve.
Improve product quality; Inform strategic decisions; Enable proactive detection; Drive measurable improvements; Accelerate insight generation; Automate analytical workflows
Industry & Context.
Root cause analysis; Troubleshooting; Data-driven decision making
What They're Looking For.
Must Have
3+ years of data scientist experience, 3+ years of data querying languages experience, 3+ years of scripting languages experience, 3+ years of statistical/mathematical software experience, 3+ years of machine learning/statistical modeling experience, 1+ years of working with or evaluating AI systems experience
Nice to Have
Ph. D. in STEM, Knowledge of machine learning concepts, Experience defining GenAI benchmarks, Experience working on multi-team projects, Experience applying quantitative analysis, Experience developing experimental and analytic plans
What You'll Do.
Implement AI/ML models
Design anomaly detection systems
Implement anomaly detection systems
Design statistical frameworks
Implement statistical frameworks
Analyze customer interactions
Identify emerging issues
Drive measurable improvements
Own model development lifecycle
Perform exploratory analysis
Perform feature engineering
Partner with product teams
Partner with engineering teams
Partner with science teams
Translate business problems
Translate technical problems
Deliver data science solutions
Guide strategic decisions
Develop analytical solutions
Develop impact measurement solutions
Develop KPI development solutions
Develop metric integrity validation solutions
Develop long-term business monitoring solutions
Explore GenAI approaches
Apply GenAI approaches
Explore LLM-based approaches
Apply LLM-based approaches
Accelerate insight generation
Automate analytical workflows
Solve smart home challenges
Communicate recommendations
Influence product strategy
Influence business strategy
Collaborate with colleagues
Contribute to science community
Improve data workflows
How You'll Work.
Team & Collaboration
Partner with engineering; Partner with product; Partner with science; Collaborate with colleagues; Cross-disciplinary teams
Communication Scope
Data narratives; Written documents; Presentations
Full Job Description
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 we're 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 inte
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