Jobs via Dice
Data Scientist || Seattle, WA (Onsite) || Fulltime
Jobs via Dice, Seattle, Washington, us, 98127
Role
Data Scientist Location
Seattle, WA (Onsite) Experience
10+ Years Job Description
Data Analysis and Exploration: Analyze large, complex datasets to extract meaningful insights and identify trends. Perform exploratory data analysis (EDA) using AWS data processing tools. Model Development: Build, train, and evaluate machine learning models using AWS services such as SageMaker, and frameworks like TensorFlow. ETL and Data Preparation: Work with AWS Glue, Redshift, Textract and other data engineering tools to preprocess, transform, and manage data for machine learning purposes. Machine Learning Pipeline Development: Develop end-to-end machine learning pipelines on AWS to automate and operationalize the deployment of models at scale. Collaboration: Work closely with data engineers, business analysts, and stakeholders to understand business needs and tailor data science solutions to meet those needs. Model Deployment and Monitoring: Deploy models to production and set up monitoring systems to track performance, accuracy, and other key metrics. Use SageMaker and Lambda for model hosting and API development. Documentation and Reporting: Document models, processes, and findings for stakeholders, enabling clear communication of results and decision support.
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Data Scientist Location
Seattle, WA (Onsite) Experience
10+ Years Job Description
Data Analysis and Exploration: Analyze large, complex datasets to extract meaningful insights and identify trends. Perform exploratory data analysis (EDA) using AWS data processing tools. Model Development: Build, train, and evaluate machine learning models using AWS services such as SageMaker, and frameworks like TensorFlow. ETL and Data Preparation: Work with AWS Glue, Redshift, Textract and other data engineering tools to preprocess, transform, and manage data for machine learning purposes. Machine Learning Pipeline Development: Develop end-to-end machine learning pipelines on AWS to automate and operationalize the deployment of models at scale. Collaboration: Work closely with data engineers, business analysts, and stakeholders to understand business needs and tailor data science solutions to meet those needs. Model Deployment and Monitoring: Deploy models to production and set up monitoring systems to track performance, accuracy, and other key metrics. Use SageMaker and Lambda for model hosting and API development. Documentation and Reporting: Document models, processes, and findings for stakeholders, enabling clear communication of results and decision support.
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