About the job
The Sr. Applied Scientist Pricing Optimization, Leo Global Pricing Strategy will have an outsized impact on the profitability of Amazon Leo directly through building the initial foundational models to enable the business to optimize our pricing and product feature strategies. You will be building a true "0 to 1" function, powering the science behind pricing decisions and driving science automation at a global scale.
Responsibilities
You will design, develop, and deploy advanced machine-learning models to predict customer-level behavior (revenue, churn, usage, migration, product choice) and response to pricing changes. You will build robust models that capture the complexities of multi-product bundling interactions in a subscription business model and regional nuances in supply/demand and consumer choice alternative choices. You will implement scalable inference systems, will monitor model performance, and will automate retraining. Collaboration with cross-functional teams will be critical to ensure that technical solutions will align with business objectives and actionable strategies. Establishing mechanisms to stay up to date on latest scientific advancements in machine learning, neural networks, natural language processing, probabilistic forecasting, and multi-objective optimization techniques will be critical.
Qualifications
Minimum
3+ years of building machine learning models for business application experience
PhD, or Master's degree and 6+ years of applied research experience
Experience programming in Java, C++, Python or related language
Experience with neural deep learning methods and machine learning
Preferred
Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
Experience with large scale distributed systems such as Hadoop, Spark etc.
Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability