Data Scientist, Amazon

• 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

Desirable

• 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
• 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