Data Scientist, Sales Insights, Analytics, Data Engineering & Science (SIADS), Sales Insights, Analytics, Data Engineering & Science (SIADS) at Amazon
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Amazon
Data Scientist, Sales Insights, Analytics, Data Engineering & Science (SIADS), Sales Insights, Analytics, Data Engineering & Science (SIADS)
US, TX, Austin
machine learning
AGI
RAG
AWS is seeking an experienced, self-directed Data Scientist to support Sales, Strategy, and Operations. They will be responsible for finding new ways of leveraging our large, complex data streams to help us serve our customers in their journey to the cloud.
A successful candidate will collaborate closely with business stakeholders, product managers, and data engineers on high visibility and high impact initiatives. They will invent, implement, and deploy state of the art machine learning/AI algorithms and systems to understand our data using tools and techniques such as causal inference models. They will build prototypes and explore conceptually large-scale ML solutions. Beyond mathematical understanding, they have a deep intuition for machine learning that allows them to discover new insights and optimize our sales intelligence offerings. They are able to pick up and grasp new research and identify applications or extensions within the team. They are a superb written and verbal communicator.
Key job responsibilities
Work with business stakeholders, product managers, data scientists, and engineers to translate business problems into the right machine learning, data science, and/or statistical solutions.
Execute every stage of the machine learning development life cycle; researching, developing, deploying, scheduling in production, measuring adoption, improving, and maintaining.
Build state of the art causal inference models to help the business understand its key drivers
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.
Communicate insights to business owners in concise, non-technical language.
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?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
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
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
About Sales, Marketing and Global Services (SMGS)
SMGS is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector.
A successful candidate will collaborate closely with business stakeholders, product managers, and data engineers on high visibility and high impact initiatives. They will invent, implement, and deploy state of the art machine learning/AI algorithms and systems to understand our data using tools and techniques such as causal inference models. They will build prototypes and explore conceptually large-scale ML solutions. Beyond mathematical understanding, they have a deep intuition for machine learning that allows them to discover new insights and optimize our sales intelligence offerings. They are able to pick up and grasp new research and identify applications or extensions within the team. They are a superb written and verbal communicator.
Key job responsibilities
Work with business stakeholders, product managers, data scientists, and engineers to translate business problems into the right machine learning, data science, and/or statistical solutions.
Execute every stage of the machine learning development life cycle; researching, developing, deploying, scheduling in production, measuring adoption, improving, and maintaining.
Build state of the art causal inference models to help the business understand its key drivers
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.
Communicate insights to business owners in concise, non-technical language.
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?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
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
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
About Sales, Marketing and Global Services (SMGS)
SMGS is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector.