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Limitless High-tech career opportunities - Expoint

Amazon Applied Scientist Books Advertising 
United States, Virginia 
715357629

24.06.2024


- Participate in end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity.
- Effectively perform hands-on analysis and research of large data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience.
- Contribute to build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models.
- Run A/B experiments, gather data, and perform statistical analysis.
- Help establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.
- Research new and innovative machine learning approaches.** Due to the RTO policy, Candidates have to be based at Arlington County/Virginia (HQ2), or be willing to relocate to HQ2**Key job responsibilities
* Actively participate in or lead the applied science projects involving Generative AI and/or other cutting-edge technologies to address customer's painpoints
* Conduct advanced and insightful data analytics to reveal business opportunities and contribute to the roadmap of the team
* Deliver models, experiments, OE artifacts to drive impact
* Author whitepapers, tech docs and conference papers to influence the science community.A day in the life
A typical day in the life of this position usually involves highly effective analytics and modeling during a period of focus time, engaging with PMs and stakeholders to communicate problem scopes, statuses, challenges and solutions, working with peers to brainstorm ideas, and documenting material achievements, observations and results.Arlington, VA, USA

BASIC QUALIFICATIONS

- * Quip docs on previous work
- * AMLC or other conference/journal publications
- * Code samples


PREFERRED QUALIFICATIONS

- Quip docs for previous projects