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Key job responsibilitiesExecute every stage of the machine learning development life cycle; researching, developing, deploying, scheduling in production, measuring adoption, improving, and maintaining.Work with large volumes of structured and unstructured data spread across multiple databases. Design and implement data pipelines to clean and merge these data for research and modeling.
Use AWS services (AWS Redshift, S3, EC2, Glue, etc) to deploy scalable ML models in the cloud.Examples of projects include: propensity-to-buy prediction and explanation, product recommendation, forecasting, anomaly detection, text classification, generative AI content generation
About the team
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.Work/Life Balance
About Sales, Marketing and Global Services (SMGS)
- 2+ 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
- Experience applying theoretical models in an applied environment
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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