Amazon

DESCRIPTION

The Pricing and Promotions Optimization Science team is hiring an incrementality applied scientist with experience in causal inference, experimentation, and ML development to help us expand our causal modeling solutions for understanding promotion effectiveness. Our work is foundational to providing seller-facing promotional tools, furthering internal research & development, and building out Amazon's promotion optimization measurement offerings. Incrementality measurement is a lynchpin for the next generation of Amazon Promotion solutions and this role will play a key role in the release and expansion of these offerings.

• Partner with principals and senior team members to drive science improvements and implement technical solutions at the state-of-the-art of machine learning and econometrics
• Partner with engineering and other science collaborators to design, implement, prototype, deploy, and maintain large-scale causal ML models.
• Carry out in-depth research and analysis exploring promotion-related data sets, including large sets of real-world experimental data, to understand behavior, highlight model improvement opportunities, and understand shortcomings and limitations.
• Define data quality standards for understanding typical behavior, capturing outliers, and detecting model performance issues.
• Work with product stakeholders to help improve our ability to provide quality measurement of promotion effectiveness for our customers.

About the team

The Pricing and Promotions Optimization science team owns price quality, discovery and discount optimization initiatives across Amazon’s internal pricing and promotions architectures as well as upwards into the customer discovery funnel. We leverage planet scale data on billions of Amazon and external competitor products to build advanced optimization models for pricing, elasticity estimation, product substitutability, and optimization. We preserve long term customer trust by ensuring Amazon's prices and promotions are always competitive and error free.

BASIC QUALIFICATIONS

• 3+ years of building models for business application experience
• 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 designing experiments and statistical analysis of results

PREFERRED QUALIFICATIONS

• Experience in professional software development
• Knowledge of architectural concepts and algorithms, schedule tradeoffs and new opportunities with technical team members
• 2+ years of designing experiments and statistical analysis of results experience
• 2+ years of hands-on predictive modeling and large data analysis experience

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $223,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.