Senior Pricing Scientist (Causal ML & Forecasting)

Amazon; Seattle (WA); On-site; USD 167,100 - 226,100; Full time

Job summary

Amazon seeks a Sr. Applied Scientist for Pricing Optimization to advance price prediction models using deep learning, transformer architectures, and causal ML. You will collaborate across pricing, product and engineering teams to deploy scalable ML solutions and rigorous experimentation.

The role emphasizes innovation, rigorous statistical analysis, and cross‑functional impact. The team combines deep learning, probabilistic forecasting, and causal modeling to measure pricing decisions on

Qualifications

• 4+ years of applied research experience.
• 3+ years building ML models for business applications.
• PhD or Master’s with 6+ years of applied research experience.
• Experience programming in Java, C++, Python or related language.
• Experience with neural deep learning methods and ML.

Responsibilities

• See the big picture and influence long‑term pricing strategy using deep learning and causal methods.
• Collaborate with product, engineering and science teams to deploy ML price estimation and corrections at scale.
• Stay current with advances in deep learning, transformers, probabilistic forecasting, and multi‑objective optimization.
• Foster rapid experimentation and data‑driven decision making to improve pricing decisions.
• Execute and deliver using deep neural networks and attention models to improve pricing problems.

Skills

Applied research

ML model building

Programming (Java/C++/Python)

Deep learning

Education

PhD

Master's + 6+ years applied research

Tools

R

scikit-learn

Spark MLlib

MxNet

TensorFlow

NumPy

SciPy

Job description

Amazon seeks a Sr. Applied Scientist for Pricing Optimization to advance price prediction models using deep learning, transformer architectures, and causal ML. You will collaborate across pricing, product and engineering teams to deploy scalable ML solutions and rigorous experimentation.

The role emphasizes innovation, rigorous statistical analysis, and cross‑functional impact. The team combines deep learning, probabilistic forecasting, and causal modeling to measure pricing decisions on