Job Summary
Develop personalized experiences for Amazon customers as they shop, creating a huge impact by helping build a state-of-the-art recommendation system.
Key Responsibilities
* Design, develop, evaluate, deploy, and update data-driven models for shopping personalization.
* Apply supervised and uplift learning techniques to improve ML performance.
* Research and implement ML and statistical approaches to add value to the business.
* Design A/B tests and conduct statistical analysis on their results.
* Apply machine learning and statistical algorithms to harness enormous volumes of data as we serve our customers.
A Day in the Life
As a Senior Data Scientist in the MAPLE team, your day might start with a stand-up meeting, aligning priorities with your colleagues. You'll then dive into analyzing the results of a recent A/B test on a new recommendation algorithm you've developed. Midday, you might collaborate with engineers to optimize the implementation of your model for production. In the afternoon, you could find yourself mentoring a junior team member on statistical techniques or presenting your latest findings to business stakeholders.
About the Team
Our team's mission is to surface the right payments-related recommendations to customers at the right time, helping create a rewarding and successful shopping experience for Amazon's customers. Our team's culture is highly collaborative, with an emphasis on supporting each other and learning from one another.
Basic Qualifications
* 6+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience.
* 6+ years of data scientist experience.
* Experience with statistical models e.g. multinomial logistic regression.
* 6+ years of data scientist or similar role involving data extraction, analysis, statistical modeling and communication experience.
* Strong understanding of statistical concepts, hypothesis testing, and causal learning.
Preferred Qualifications
* Experience managing data pipelines.
* Experience as a leader and mentor on a data science team.
* Ability to autonomously lead complex data science projects to completion and drive business impact.
* Experience with developing recommender systems.
* Experience analyzing A/B tests and drawing insights from them.