Amazon "Buy for Me" is seeking a talented and experienced Data Scientist to join our data analytics team. This is a key technical role and we are seeking someone who is passionate about working on large-scale data challenges and driving innovation in ML/AI data science practice at scale that solves real-world customer issues.
This role is critical to ensuring strong analytical capabilities leveraging of emerging technologies, building best practices in data science, and collaborating with key stakeholders to support a deep understanding of the customer experience, solving the analytical problems and product issues.
The ideal candidate will have extensive experience working in complex data and analytics environments at scale, driving solutions to develop better data science practices. Experience with Machine Learning and other advanced analytics techniques is a must and you also need to be able to talk about these technical concepts in plain terms. The role requires both technical depth and the ability to lead and communicate effectively to stakeholders.
Key job responsibilities
Lead the adoption of data-driven and ML-enabled solutions across the analytics and business intelligence practice, identifying high-impact opportunities to automate workflows, improve decision-making, and drive business outcomes.
Partner with product, engineering, science, and business stakeholders to design and launch data products, predictive models, and reporting pipelines that enhance customer experience and operational efficiency.
Translate ambiguous or complex business problems into structured analytical approaches, combining exploratory data analysis, hypothesis testing, and statistical modeling.
Develop and maintain scalable data pipelines, data models, and dashboards to support self-service analytics and executive reporting.
Implement data validation, monitoring, and experimentation frameworks to ensure the accuracy and reliability of metrics and model outputs.
Collaborate closely with data engineers to align on infrastructure, data quality, and performance optimization for analytics and ML use cases.
Mentor and support the growth of analysts, BIEs, and junior data scientists by sharing best practices in data science, analytics engineering, and experimentation.
Stay current on industry trends in business intelligence, analytics, and applied machine learning; champion adoption of scalable tools and methods where appropriate.
