Sr. Applied Scientist Pricing Optimization, Leo Global Pricing Strategy

Amazon Amazon · Big Tech · Bellevue, WA · Data Science

The Sr. Applied Scientist Pricing Optimization role at Amazon Leo focuses on building foundational ML models to optimize pricing and product feature strategies for a satellite broadband network. This involves predicting customer behavior, modeling multi-product bundling, and implementing scalable inference systems.

What you'd actually do

  1. design, develop, and deploy advanced machine‑learning models to predict customer‑level behavior (revenue, churn, usage, migration, product choice) and response to pricing changes
  2. 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
  3. implement scalable inference systems, will monitor model performance, and will automate retraining
  4. Collaboration with cross‑functional teams will be critical to ensure that technical solutions will align with business objectives and actionable strategies
  5. 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

Skills

Required

  • 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

Nice to have

  • 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

What the JD emphasized

  • building the initial foundational models
  • critical to ensure that technical solutions will align with business objectives
  • critical

Other signals

  • building the initial foundational models
  • optimize our pricing and product feature strategies
  • science behind pricing decisions
  • science automation at a global scale