Sr. Applied Scientist, Pricing Science

Here"s the job description with causal ML woven in:

We are looking for a talented, organized, and customer-focused applied researcher to join our Pricing Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon.

This role requires an individual with exceptional machine learning modeling and architecture expertise - particularly in deep learning, neural networks, and transformer-based architectures applied to price prediction and forecasting problems. Equally important is deep expertise in causal machine learning - including causal inference, treatment-effect estimation, and experimentation methods (e.g., uplift modeling, double/debiased machine learning, instrumental variables, and A/B and quasi-experimental design) - to isolate the true impact of pricing and promotion decisions on customer behavior and business outcomes. The ideal candidate brings a strong foundation in applied statistics and probabilistic modeling, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit.

We are looking for an experienced innovator who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment.

Key job responsibilities

See the big picture. Understand and influence the long-term vision for Amazon"s science-based competitive, perception-preserving pricing techniques. Develop and advance price prediction models leveraging deep learning frameworks, transformer architectures, and advanced statistical methods to drive pricing accuracy at scale.

Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale. Design and implement neural network-based architectures - including sequence models and transformers - for large-scale price prediction and optimization.

Stay informed. Establish mechanisms to stay up to date on the latest scientific advancements in deep learning, transformer architectures, applied statistics, neural network design, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems.

Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery. Leverage statistical rigor and modern deep learning approaches to validate hypotheses and drive measurable pricing improvements.

Successfully execute & deliver. Apply your exceptional technical machine learning expertise - including deep neural networks, attention-based models, and applied statistical analysis - to incrementally move the needle on some of our hardest pricing problems.

A day in the life

We are hiring a Sr. Applied Scientist to drive our pricing optimization initiatives. We drive cross-domain and cross-system improvements through:

• shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs.
• Error detection and price quality guardrails at scale.
• Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods)

Price is a highly relevant input into Stores architectures; this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication.

About the team

The Pricing Optimization science group builds and refines Amazon"s algorithmic pricing and promotion models at scale. Our team combines expertise in deep learning, transformer architectures, applied statistics, and probabilistic forecasting to develop price prediction systems that directly impact the customer experience. The team also brings hands-on experience with causal modeling and inference - including uplift modeling and treatment effect estimation - to rigorously measure the impact of pricing decisions on customer behavior and business outcomes. We partner closely with product, engineering, and business teams to take solutions from research through production deployment.