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Key job responsibilities
• Collaborate with our applied and data scientists to build robust and scalable Generative AI solutions for business problems
• Effectively use Foundation Models available on Amazon Bedrock and Amazon SageMaker to meet our customer's performance needs
• Work hands on to build scalable cloud environment for our customers to label data, build, train, tune and deploy their models
• Interact with customer directly to understand the business problem, help and aid them in implementation of their ML ecosystem
• Analyze and extract relevant information from large amounts of historical data to help automate and optimize key processes
• Work closely with partner teams to drive model implementations and new algorithmsAbout AWSDiverse 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.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
Hybrid Work
- 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
- 4+ years of data scientist experience
- Experience with statistical models e.g. multinomial logistic regression
- 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience
- Experience managing data pipelines
- Experience as a leader and mentor on a data science team
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