Amazon.com Services LLC
E-Commerce
Economist,PricingScience
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Economist, Pricing Science at Amazon.com Services LLC. Skills: Data Science, Machine Learning, Statistical Modeling. Design, develop, and deploy machine learning models. Conduct statistical analysis and A/B testing”
Industry & Context.
Data-driven decision making
What They're Looking For.
Must Have
5+ years of experience in data science or related field, Bachelor's degree in Computer Science, Statistics, Mathematics, or related quantitative field, Proficiency in SQL, Experience with Python or R for data analysis and modeling
Nice to Have
Master's degree or PhD in a quantitative field, Experience with large-scale data processing frameworks (e.g., Spark), Experience with cloud platforms (AWS, GCP, Azure), Experience with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch), Experience with A/B testing and experimental design, Experience with causal inference methods
What You'll Do.
and deploy machine learning models
Conduct statistical analysis and A/B testing
Develop and maintain data pipelines
Collaborate with product and engineering teams
Communicate findings and recommendations to stakeholders
Stay current with advancements in data science and
How You'll Work.
Team & Collaboration
Product teams; Engineering teams; Stakeholders
Full Job Description
Estimating the demand response of a pricing decision is genuinely hard. The causal effects are delayed, noisy, and confounded by factors that standard experiment analysis wasn't designed to handle. Most pricing teams default to heuristics not because they don't care about customer responses, but because measuring them rigorously is an unsolved problem. P2OS is building the science to solve it. We're hiring an Economist to own that work — defining how we estimate digital demand response in a pricing context, building the identification strategies that make those estimates credible, and translating outputs into something pricing teams can use to make better decisions. The role sits at the intersection of econometric methodology and production-quality analysis, and requires someone who can operate independently in both. As science lead, you'll own the digital pricing methodology domain, and be the internal authority on causal inference for pricing across P2OS and partner teams. Key job responsibilities * Own the end-to-end digital pricing methodology for pricing — identification strategy, modeling choices, validation approach, and business use cases — and drive adoption across pricing contexts * Deliver high-stakes analyses connecting digital pricing estimates to a concrete pricing decision and strategy change at VP+ level * Apply advanced causal methods to live pricing problems; document approaches so the team can build on and extend them. * Provide causal inference guidance on pricing experiment questions as they arise — being the methodology resource when experiments generate relevant questions * Serve as cross-team economic advisor to Digital Finance, Customer Behavior, and Demand Science on assumptions and causal identification * Actively mentor junior scientists, earn trust of cross-functional tech and product partners. A day in the life In a typical day, you'll move between methodology work and stakeholder-facing analysis. - On the science side, that means revie
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