We are seeking a talented and eager-to-learn Entry-Level Applied Scientist to join our team and contribute to the development of cutting-edge machine learning solutions for our customers in the MENA region. As an Entry-Level Applied Scientist, you will have the opportunity to apply off-the-shelf algorithms to well-defined problems, expand your science skills, and make meaningful contributions to the team.
Key job responsibilities: Develop machine learning models, run A/B experiments, and perform statistical analysis to provide insights that drive business objectives.Collaborate with cross-functional teams, including product managers, engineers, and more experienced applied scientists, to identify high-impact areas for innovation.Proactively seek to identify business opportunities and insights and provide solutions to automate and optimize key business processes and policies based on a broad and deep knowledge of Amazon data, industry best-practices, and work done by other teams.Establish scalable, efficient, and automated processes for large-scale data analysis, model development, validation, and deployment.Analyze and extract relevant insights from large datasets using a variety of techniques, including machine learning, data mining, and statistical analysis. A day in the life: Applying relevant science solutions for well-defined problem statements. Collaborating with other scientists in the team and enhancing the solutions.
About the team: The MENA Science team works closely with the business and engineering teams in building ML solutions that create an impact for MENA businesses. This is a great opportunity to leverage your machine learning and data mining skills to create a direct impact on consumers and end users.
Minimum Requirements: Master's degree in a quantitative field such as computer science, mathematics, statistics, machine learning, or an equivalent quantitative field, or Bachelor's degree and 2+ years of relevant experience.Experience with coding in Python or any relevant scripting language.Experience in applying machine learning solutions to real-world problem statements.Basic understanding of any one of the following: large language models, NLP (Information retrieval, Machine Translation), Computer Vision, Classification models using Boosting/Bagging or Deep Neural Networks.
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