Job Information
Amazon Sr. Data Scientist, Search Experimentation and ML in Palo Alto, California
Description
The Amazon Search Relevance team is responsible for helping customers find relevant products that exceed their expectations in terms of price, quality, and convenience. Our goal is to make shopping easy and enable customers to shop with confidence. The Search Relevance Data Science team helps with the goal by deeply understanding customer interactions with Search, and helping translate those understandings into insights. Teams within Search and outside use these insights to build a delightful search experience for Amazon customers WW.
The Data Science team works on metrics, analysis, and insights that inform Amazon’s leaders on how the largest discovery platform for retail, viz. organic search, is performing.
Key job responsibilities
You will build key analyses, dashboards, and reports that help Search Relevance team understand the short- and long-term impact of their weblabs. You will collaborate with DS, AS, Economists, and PMs to help understand the levers that drive the search experience.
You will be responsible for calling out and explaining movements in key business and customer metrics in monthly and quarterly business reviews. You will have a working knowledge of the data available or needed by the wider business for more complex or comparative analysis.
You will proactively and independently work with stakeholders to construct use cases and associated standardized outputs. You will translate difficult business problem statements into analysis requirements and maintain a high bar throughout the execution.
You will actively manage the timeline and deliverables of projects, focusing on interactions in the team, and communicate roadblocks to stakeholders and propose solutions to overcome those roadblocks.
A day in the life
Our team is a mix of AS, DS, and SDEs, focusing on problems in search relevance. We spend our time in building ML models, building metrics and tools to better understand them offline and online, and running experiments. We take care in communicating our results broadly, informing and educating a large variety of stakeholders.
We are open to hiring candidates to work out of one of the following locations:
Palo Alto, CA, USA
Basic Qualifications
5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
4+ years of data scientist experience
Experience with statistical models e.g. multinomial logistic regression
Experience working with scientists, economists, software developers, or product managers
Preferred Qualifications
2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience
Experience managing data pipelines
Experience as a leader and mentor on a data science team
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. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $143,300/year in our lowest geographic market up to $247,600/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.