Offers “Amazon”

Expires soon Amazon

Data Scientist, Alexa International

  • Internship
  • Luxembourg (Canton Luxembourg)

Job description



DESCRIPTION

Alexa International is looking for a Data Scientist to help improve customer experience by driving insights from Alexa's usage patterns. As part of the Alexa International Business Intelligence team, you will have the opportunity to work on one of the world's largest data sets and influence the long-term evolution of Alexa features.

Key Responsibilities:

· Develop routine reporting, ad hoc, statistical inference and predictive modeling and scalable algorithms and models.
· Analyze complex datasets to make actionable recommendations
· Create visualizations to drive data insight. Collaborate with Finance and Business partners to develop clear business and measurement objectives
· Ensure that the quality and timeliness of analytic deliverables meet Alexa’s expectations
· Develop and present papers with insights and recommendations to senior audiences
· Provide informal direction and guidance to more junior members of staff on all aspects of analytics from business context and prioritization to technical coaching

PREFERRED QUALIFICATIONS

· PhD in a quantitative field such as Economics, Mathematics, Information Systems, Statistics, Operations Research or Computer Science
· Few years of experience in data science, business intelligence or machine learning engineer position
· Experience in building chatbots, AI assistants, working knowledge of reinforcement learning and deep learning applications
· Verbal/written communication & data presentation skills, including an ability to effectively communicate with both business and technical teams.
· Experience solving complex quantitative business problems.

Desired profile



BASIC QUALIFICATIONS

· Advanced degree in computer science, statistics, information systems, economics, mathematics or similar
· Few years of experience working with large-scale, complex datasets to create/optimize machine learning, predictive, forecasting, and/or optimization models
· Strong proficiency in SQL
· Strong R/Python skills
· Very strong self-learning skills. Ability to pick up and adapt modeling methods from other disciplines or leverage methods from colleagues in other departments.
· Practical understanding and hands-on experience with the following:
· Supervised learning methods (linear and logistic regression, time-series modelling, generalized linear models, decision trees, random forests, support vector machines, etc.).
· Unsupervised learning methods (K-means, hierarchical clustering, association rules, principal components).
· Mathematical optimization (mixed integer programming, linear programming).

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