Applied Scientist, Pricing Science

Key job responsibilities

• Build causal ML pipelines for pricing
• Design, train, evaluate, and deploy end-to-end causal estimation models for pricing use cases
• Own the science on heterogeneous treatment effects
• Be the team SME on causal ML methodology: identification strategies, model selection, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation
• Support pricing experiment analysis
• Contribute causal analysis methodology to pricing weblab and A/B test post-analysis
• Build reusable tooling that economists can use without requiring ML expertise
• Define, before writing code, what business metric each model moves
• Deliver model evaluation reports framed around pricing errors avoided and LTV estimate changes
• Evaluate and adopt novel techniques
• Assess applicability of emerging causal inference methods (synthetic DiD, generalized random forests, causal representation learning) to Amazon's pricing context
• Write internal methodology proposals for adoption
• Evaluate and adopt novel techniques
• Write internal documentation and methodology papers
• Connect model outputs to business outcomes
• Produce at least one internal write-up per half that connects a causal ML technique to a concrete pricing use case
• Make pipelines extensible and well-documented so other scientists can build on them
• Collaborate across disciplines
• Partner closely with the Sr. Economist on identification strategy and causal assumptions
• Work with SDE and DE partners on production deployment
• Align with PMs on experiment design requirements

Basic Qualifications

• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• Experience programming in Java, C++, Python or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred Qualifications

• Experience using Unix/Linux
• Experience in professional software development
• Usage of generative AI tools to enhance workflow efficiency, with a willingness to learn effective prompting and evaluation practices
• Ability to recognize opportunities where generative AI could enhance products, workflows, or customer experiences