Description
- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- Bachelor's degree in sciences, engineering, finance or equivalent
- 4+ years of solving business problems through machine learning, data mining and statistical algorithms experience
- Experience communicating results to senior leadership, or experience in solving complex business challenges by delivering accurate and timely financial models, analysis, and recommendations that have a proven impact on business (e.g., financial savings, operational improvements, or customer benefits)
- Experience in managing global stakeholders
- Experience in Strong hands-on programming skills in Python, SQL, Hadoop/Hive. Additional knowledge of Spark, Scala, R, Java desired but not mandatory.
- Master's degree in machine learning or equivalent
- Experience with training and deploying machine learning systems to solve large-scale optimizations, or experience in technical support
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
As a Research Analyst, you'll collaborate with experts to develop and own advance machine learning and AI solutions for business needs. You'll drive product pilots, demonstrating innovative thinking and customer focus. You'll build scalable solutions, write high-quality code, and develop state-of-the-art AI models. You'll coordinate between science and software teams, optimizing solutions. The role requires thriving in ambiguous, fast-paced environments and working independently with AI models.
Key job responsibilities
• Collaborate with Applied Scientists to implement ML/LLM solutions that meet business goals
• Conduct product pilots demonstrating customer obsession and innovation
• Develop scalable solutions by writing high-quality code, building ML/LLM models using current research breakthroughs and implementing performance optimization techniques
• Act as a bridge between science and software teams to deliver optimized solutions
• Communicate technical concepts to stakeholders at all levels
• Develop technical documentation for Design specifications, Algorithms, Implementation challenges and Performance metrics
• Monitor and maintain existing solutions to ensure peak performance
About the team
The Retail Business Systems (RBS) group is an integral part of Amazon online product lifecycle and buying operations. The team is designed to ensure Amazon remains competitive in the online retail space with the best catalog quality, wide selection, supply chain defects and compliance programs. The team’s primary role is to create and enhance retail selection on the worldwide Amazon online catalog. The tasks handled have a direct impact on customer buying decisions and online user experience.
- Bachelor's degree in sciences, engineering, finance or equivalent
- 4+ years of solving business problems through machine learning, data mining and statistical algorithms experience
- Experience communicating results to senior leadership, or experience in solving complex business challenges by delivering accurate and timely financial models, analysis, and recommendations that have a proven impact on business (e.g., financial savings, operational improvements, or customer benefits)
- Experience in managing global stakeholders
- Experience in Strong hands-on programming skills in Python, SQL, Hadoop/Hive. Additional knowledge of Spark, Scala, R, Java desired but not mandatory.
- Master's degree in machine learning or equivalent
- Experience with training and deploying machine learning systems to solve large-scale optimizations, or experience in technical support
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
As a Research Analyst, you'll collaborate with experts to develop and own advance machine learning and AI solutions for business needs. You'll drive product pilots, demonstrating innovative thinking and customer focus. You'll build scalable solutions, write high-quality code, and develop state-of-the-art AI models. You'll coordinate between science and software teams, optimizing solutions. The role requires thriving in ambiguous, fast-paced environments and working independently with AI models.
Key job responsibilities
• Collaborate with Applied Scientists to implement ML/LLM solutions that meet business goals
• Conduct product pilots demonstrating customer obsession and innovation
• Develop scalable solutions by writing high-quality code, building ML/LLM models using current research breakthroughs and implementing performance optimization techniques
• Act as a bridge between science and software teams to deliver optimized solutions
• Communicate technical concepts to stakeholders at all levels
• Develop technical documentation for Design specifications, Algorithms, Implementation challenges and Performance metrics
• Monitor and maintain existing solutions to ensure peak performance
About the team
The Retail Business Systems (RBS) group is an integral part of Amazon online product lifecycle and buying operations. The team is designed to ensure Amazon remains competitive in the online retail space with the best catalog quality, wide selection, supply chain defects and compliance programs. The team’s primary role is to create and enhance retail selection on the worldwide Amazon online catalog. The tasks handled have a direct impact on customer buying decisions and online user experience.
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