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    Job · 20660042

    BENGALURU · FULLTIME

    Sr. Applied Scientist, Alexa Excellence AI Ops, Alexa Excellence AI Ops

    Amazon

    Amazon

    Bengaluru · onsite · Posted 11d ago

    Sourced fromFoundit
    Not disclosed3–8 yrsfulltimeBengaluru

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    Section · 01

    About this role

    Description

    We are seeking a Senior Applied Scientist to join the Alexa Availability team within Alexa Excellence. This role leads the research and development of machine learning and statistical models that power Alexa's reliability at massive scale — serving hundreds of millions of customers globally.

    The ideal candidate will tackle complex, ambiguous problems spanning time series multivariate modeling, statistical anomaly detection, LLM-based operational intelligence, and adaptive threshold systems. They will design production-grade ML solutions, establish rigorous evaluation frameworks, and ensure AI systems are grounded, reliable, and free from systematic bias — leveraging techniques such as RAG, confidence scoring, knowledge graph integration, and counterfactual testing.

    This scientist will partner with engineers, product managers, and operations leaders to translate scientific innovation into production systems that directly impact Alexa's availability worldwide. They will drive the scientific agenda for the team, mentor fellow scientists, and influence the broader Alexa Excellence organization through technical leadership and cross-team collaboration.

    Key Focus Areas

    Anomaly detection and predictive failure modeling

    Cross-service correlation and LLM-driven operational intelligence

    Production ML at the intersection of large-scale distributed systems and applied science

    Model reliability, hallucination mitigation, and grounding for operational AI

    Key job responsibilities

    As a Senior Applied Scientist on the Alexa Availability team, you will lead the research and development of machine learning and statistical models that power Alexa's reliability at scale. You will work on some of the most complex and ambiguous problems in the space — from time series multivariate modeling and statistical anomaly detection to LLM-based operational intelligence and adaptive threshold systems.

    A day in the life

    You will design and implement production-grade ML solutions, establish rigorous model evaluation frameworks, and ensure our LLM-powered systems are grounded, reliable, and free from systematic bias. You will apply techniques such as Retrieval-Augmented Generation (RAG), confidence scoring, knowledge graph integration, and counterfactual testing to ensure our AI systems make trustworthy operational decisions at scale.

    You will partner closely with software engineers, product managers, and operations leaders to translate scientific innovation into production systems that directly impact Alexa's availability for customers worldwide. You will drive the scientific agenda for your team, mentor fellow scientists, and influence the broader Alexa Excellence organization through your technical leadership and cross-team collaboration.

    About The Team

    The Alexa Excellence team is at the heart of delivering a world-class Alexa experience to hundreds of millions of customers globally. Within Alexa Excellence, the Alexa Availability team is responsible for ensuring Alexa is always on, always responsive, and always reliable. We own the systems, signals, and science that detect, diagnose, and drive resolution of availability issues at scale — before customers ever notice.

    We are building the next generation of intelligent availability solutions powered by machine learning, large language models, and advanced statistical modeling. Our work spans anomaly detection, predictive failure modeling, cross-service correlation, and LLM-driven operational intelligence — all operating at the scale and reliability bar that Alexa demands. We operate at the intersection of large-scale distributed systems, applied machine learning, and operational excellence, and we are looking for scientists who can bring both deep technical rigor and a bias for production impact.

    Basic Qualifications

    • 3+ years of building machine learning models for business application experience
    • PhD, or Master's degree and 6+ years of applied research experience
    • Experience programming in Java, C++, Python or related language
    • Experience with neural deep learning methods and machine learning

    Preferred Qualifications

    • Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
    • Experience with large scale distributed systems such as Hadoop, Spark etc.

    Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.

    Company - ADCI MAA 15 SEZ

    Job ID: A10421216

    Sourced from foundit

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    Section · 02

    Skills

    ScipyrStatistical modelsApache HadoopTensorflowScikit-LearnMXNetneural deep learning methodsSpark MLLibadaptive threshold systemscounterfactual testingknowledge graph integrationLLM-based operational intelligenceproduction-grade ML solutionsRAG confidence scoringstatistical anomaly detectiontime series multivariate modelingPythonNumpyJavaMachine Learning

    Section · Company

    About Amazon

    Amazon

    Amazon

    Internet

    120k+

    employees

    1994

    32 years old

    Hyderabad / Secunderabad, Telangana

    India

    About

    Amazon is an American multinational technology company focusing on e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.

    Industries

    InternetRetailUrban TransportLogisticsEmerging TechnologiesIT Services & Consulting

    Employee ratings

    31,409 reviews

    3.9/ 5

    Culture

    4.0

    Career growth

    3.2

    Work-life

    3.8

    Employees rate it well for

    Work Culture3746Salary2936Work Life Balance2821Senior Management1830Career Growth1598

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