Data Scientist II, HST Health Foundation

We are seeking a talented data scientist to join the Amazon healthcare economics team. Our group of economists, data scientists, and analysts work closely with engineers to tackle a wide range of questions including pricing, experimentation science, personalization, data-driven product strategy/optimizations, internal productivity/incentives, and impact/ROI measurement. The ideal candidate will excel at (and enjoy) wearing many hats, and be able to effectively translate business questions into tractable quantitative frameworks, dive deep into data for analytical insights, and represent the team in a range of settings - from reviews with senior Amazon scientists to reviews with senior Healthcare/Amazon business leaders. As a member of this team, you will dive deep into our data and harness your strong data extraction, transformation, and load (ETL) skills to create data structures, build machine learning models, incorporate generative AI into business decision making, create forecasting , predictive and prescriptive models, build dashboards and visualization to support business strategy and enable GDSP to achieve its goals. If you are passionate about leveraging data-driven insights to shape the future of digital media, if you are motivated to tackle ambiguous situations with technologies to rapidly solve problems, if you are a team player, contribute to the execution of team objectives, and leverage technical knowledge and engineering best practices to rapidly deliver solutions that have a broad business impact, we encourage you to apply and be a part of our dynamic team.

Key job responsibilities
- Support the development of continuously-evolving business analytics and data models, own the quantitative analysis of the performance of our doctors, partners, markets, and products/services in context of private pricing.
- Use machine learning, data mining, and statistical techniques to design/run experiments that solve complex business problems.
- Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.
- Build data pipelines, and identify novel data sources to leverage in analytical work – both from within Healthcare/Amazon and from 3P sources
- Force multiply the work of the team with compelling data visualizations, presentations, and/or dashboards to drive awareness and adoption of data assets/products/insights
- Raise the bar for science/analytical work, both within Healthcare and across the broader Amazon community

About the team
Amazon's mission is to make it dramatically easier for customers to access the healthcare products and services they need to get and stay healthy. Towards this mission, we (Health Store and Tech) are building the technology, products and services, that help customers find, buy, and engage with the healthcare solutions they need.

- 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
- 1+ years of guiding and coaching a group of researchers experience
- 1+ years of working with or evaluating AI systems experience
- 1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience
- Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)
- Experience applying theoretical models in an applied environment

- Master's degree or above in Science, Technology, Engineering, or Mathematics (STEM)
- Knowledge of machine learning concepts and their application to reasoning and problem-solving
- Experience in Python, Perl, or another scripting language
- Experience in a ML or data scientist role with a large technology company
- Experience in defining and creating benchmarks for assessing GenAI model performance
- Experience working on multi-team, cross-disciplinary projects
- Experience applying quantitative analysis to solve business problems and making data-driven business decisions
- Experience effectively communicating complex concepts through written and verbal communication

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