Manager, Data Science, P2 Science, Data, and Insights
Amazon
DESCRIPTION
Are you looking for a role where you have the opportunity to shape Amazon’s Pricing and Promotions using data science, analytics, and insights? If so, our role in Amazon's Pricing & Promotions Science, Data, and Insights organization is for you.
Amazon's Pricing & Promotions Science, Data, and Insights organization is seeking a highly analytical Data Science Manager. In this role, you will lead a team that develops, builds, and delivers analytics, insights, scientific studies, analyses, automated analytics, insights, anomaly detection tools and technologies, which will deliver the best prices and experiences to our customers. Data science and analytics is at the core of Amazon’s culture, and your work will have a direct impact on decision making and strategy for the Pricing and Promotions organization.
This role requires a self-starting leader with high judgment that thrives in ambiguity and inspires their team. We are looking for an experienced data science leader with broad and deep scientific and analytical abilities, that is customer and delivery-focused, and a has a track record of earning your customers and peer’s trust. To be successful in this role, you will need a successful track record of leading data science and analytics teams, strong business acumen, statistics, experimentation, and an entrepreneurial mindset.
Key job responsibilities
- You will lead the data science, business intelligence, and insights team for your employees, business, and its customers.
- Mentor and grow your employees.
- Develop, build, and deliver scientific and analytical reporting, models, and business strategy.
- Lead modeling and experimentation, deep dive analyses of business problems and formulate conclusions and recommendations to be presented to senior leadership as well as published literature.
- Leverage LLMs to deploy, automate, and generate Insights and drive growth discussions with Product teams.
- Produce written scientific recommendations and insights for key stakeholders that will help shape effective metric development and reporting.
- Continuously invent, simplify, and automate your team’s products, tools, services, and methodologies. Improving back-end data sources and models for increased accuracy and simplicity.
- Recognize and adopt the best practices in data science, reporting, analyses, data quality, and modeling.