Sr. Applied Scientist Pricing Optimization, LEO Global Pricing Strategy

Amazon • Bellevue, WA

1d

About The Position

Leo is Amazon’s low Earth orbit satellite broadband network. Its mission is to
deliver fast, reliable internet to customers and communities around the world, and we’ve designed the system with the capacity, flexibility, and performance to
serve a wide range of customers, from individual households to schools,
hospitals, businesses, government agencies, and other organizations operating in
locations without reliable connectivity.

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.

Export Control Requirement:
Due to applicable export control laws and regulations, candidates must be a U.S.
citizen or national, U.S. permanent resident (i.e., current Green Card holder),
or lawfully admitted into the U.S. as a refugee or granted asylum.

A day in the life
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.

Requirements

• 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 Haves

• 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

Responsibilities

• design, develop, and deploy advanced machine‑learning models to predict customer‑level behavior (revenue, churn, usage, migration, product choice) and response to pricing changes
• 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
• 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

Benefits

• health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• paid time off
• parental leave

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level