Data Scientist III, ProdOps OAT, Project Kuiper
Job Description
Project Kuiper is Amazon’s low Earth orbit satellite broadband network. Its mission is to deliver fast, reliable internet to customers and communities around the world, and we’ve designed the system with the capacity, flexibility, and performance to serve a wide range of customers, from individual households to schools, hospitals, businesses, government agencies, and other organizations operating in locations without reliable connectivity.Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.Role OverviewAs a Senior Data Scientist (DS), you will drive the development and implementation of advanced analytics and machine learning solutions. You will work on critical initiatives including natural language processing for non-conformance analysis, statistical process controls (SPC) for test optimization, and equipment predictive maintenance models to enable manufacturing rate acceleration. Your work will directly influence Kuiper’s production manufacturing workflow. You are an analytical problem solver who enjoys diving into data from various businesses, is excited about investigations and algorithms, can multi-task, and can credibly interface between scientists, engineers, and business stakeholders. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future.Key job responsibilities- Lead the design and implementation of ML/LLM solutions to analyze manufacturing data and identify failure patterns and operational risks- Design predictive models for statistical process control and equipment maintenance optimization- Build production-ready ML pipelines leveraging AWS services (e.g., SageMaker, Bedrock, AWS Glue)- Formalize assumptions about how models are expected to behave, creating definitions of outliers, developing methods to systematically identify these outliers, and explaining why they are reasonable or identifying fixes for them- Develop and test model enhancements, running computational experiments, and fine-tuning model parameters for new models- Collaborate with engineering teams to translate complex manufacturing challenges into data-driven solutions- Drive consensus on metrics and analysis approaches to support business strategy- Write documents and create compelling visualizations and presentations to communicate insights to stakeholders- Mentor team members and drive data science best practices across the organization
Locations
- United States, WA, Redmond, Redmond, WA, United States
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
180,000 - 250,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- - 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experienceintermediate
- - 4+ years of data scientist experienceintermediate
- - Experience with statistical models e.g. multinomial logistic regressionintermediate
- - Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue)intermediate
- - Experience as a leader and mentor on a data science teamintermediate
- - Experience solving optimization problems for large organizationsintermediate
Required Qualifications
- - Bachelor's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science (degree in a quantitative field such as statistics)
- - 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience (experience, 5 years)
- - 4+ years of data scientist experience (experience, 4 years)
- - Experience with statistical models e.g. multinomial logistic regression (experience)
- - Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue) (experience)
- - Experience as a leader and mentor on a data science team (experience)
- - Experience solving optimization problems for large organizations (experience)
Preferred Qualifications
- - Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science (degree in a quantitative field such as statistics)
- - 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience (experience, 2 years)
- - Experience managing data pipelines (experience)
- - Strong ability to interact, communicate, present, and influence within multiple levels of the organization (experience)
- Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $143,300/year in our lowest geographic market up to $247,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site. (experience)
Responsibilities
- - Lead the design and implementation of ML/LLM solutions to analyze manufacturing data and identify failure patterns and operational risks
- - Design predictive models for statistical process control and equipment maintenance optimization
- - Build production-ready ML pipelines leveraging AWS services (e.g., SageMaker, Bedrock, AWS Glue)
- - Formalize assumptions about how models are expected to behave, creating definitions of outliers, developing methods to systematically identify these outliers, and explaining why they are reasonable or identifying fixes for them
- - Develop and test model enhancements, running computational experiments, and fine-tuning model parameters for new models
- - Collaborate with engineering teams to translate complex manufacturing challenges into data-driven solutions
- - Drive consensus on metrics and analysis approaches to support business strategy
- - Write documents and create compelling visualizations and presentations to communicate insights to stakeholders
- - Mentor team members and drive data science best practices across the organization
Documents
Tags & Categories
Data Scientist III, ProdOps OAT, Project Kuiper
Job Description
Project Kuiper is Amazon’s low Earth orbit satellite broadband network. Its mission is to deliver fast, reliable internet to customers and communities around the world, and we’ve designed the system with the capacity, flexibility, and performance to serve a wide range of customers, from individual households to schools, hospitals, businesses, government agencies, and other organizations operating in locations without reliable connectivity.Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.Role OverviewAs a Senior Data Scientist (DS), you will drive the development and implementation of advanced analytics and machine learning solutions. You will work on critical initiatives including natural language processing for non-conformance analysis, statistical process controls (SPC) for test optimization, and equipment predictive maintenance models to enable manufacturing rate acceleration. Your work will directly influence Kuiper’s production manufacturing workflow. You are an analytical problem solver who enjoys diving into data from various businesses, is excited about investigations and algorithms, can multi-task, and can credibly interface between scientists, engineers, and business stakeholders. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future.Key job responsibilities- Lead the design and implementation of ML/LLM solutions to analyze manufacturing data and identify failure patterns and operational risks- Design predictive models for statistical process control and equipment maintenance optimization- Build production-ready ML pipelines leveraging AWS services (e.g., SageMaker, Bedrock, AWS Glue)- Formalize assumptions about how models are expected to behave, creating definitions of outliers, developing methods to systematically identify these outliers, and explaining why they are reasonable or identifying fixes for them- Develop and test model enhancements, running computational experiments, and fine-tuning model parameters for new models- Collaborate with engineering teams to translate complex manufacturing challenges into data-driven solutions- Drive consensus on metrics and analysis approaches to support business strategy- Write documents and create compelling visualizations and presentations to communicate insights to stakeholders- Mentor team members and drive data science best practices across the organization
Locations
- United States, WA, Redmond, Redmond, WA, United States
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
180,000 - 250,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- - 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experienceintermediate
- - 4+ years of data scientist experienceintermediate
- - Experience with statistical models e.g. multinomial logistic regressionintermediate
- - Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue)intermediate
- - Experience as a leader and mentor on a data science teamintermediate
- - Experience solving optimization problems for large organizationsintermediate
Required Qualifications
- - Bachelor's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science (degree in a quantitative field such as statistics)
- - 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience (experience, 5 years)
- - 4+ years of data scientist experience (experience, 4 years)
- - Experience with statistical models e.g. multinomial logistic regression (experience)
- - Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue) (experience)
- - Experience as a leader and mentor on a data science team (experience)
- - Experience solving optimization problems for large organizations (experience)
Preferred Qualifications
- - Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science (degree in a quantitative field such as statistics)
- - 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience (experience, 2 years)
- - Experience managing data pipelines (experience)
- - Strong ability to interact, communicate, present, and influence within multiple levels of the organization (experience)
- Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $143,300/year in our lowest geographic market up to $247,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site. (experience)
Responsibilities
- - Lead the design and implementation of ML/LLM solutions to analyze manufacturing data and identify failure patterns and operational risks
- - Design predictive models for statistical process control and equipment maintenance optimization
- - Build production-ready ML pipelines leveraging AWS services (e.g., SageMaker, Bedrock, AWS Glue)
- - Formalize assumptions about how models are expected to behave, creating definitions of outliers, developing methods to systematically identify these outliers, and explaining why they are reasonable or identifying fixes for them
- - Develop and test model enhancements, running computational experiments, and fine-tuning model parameters for new models
- - Collaborate with engineering teams to translate complex manufacturing challenges into data-driven solutions
- - Drive consensus on metrics and analysis approaches to support business strategy
- - Write documents and create compelling visualizations and presentations to communicate insights to stakeholders
- - Mentor team members and drive data science best practices across the organization