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Amazon Applied Scientist MENA Stores 
India, Telangana, Hyderabad 
262910065

20.11.2024
DESCRIPTION


Key job responsibilities
• Invent, implement, and deploy cutting-edge machine learning algorithms and models to solve complex, real-world problems for our MENA customers.
• Collaborate closely with cross-functional teams, including product managers, engineers, and other applied scientists, to identify high-impact areas for innovation.
• Prototype and test new approaches, such as leveraging large language models (LLMs) and other advanced techniques, to drive measurable improvements in key business metrics.
• Tackle a diverse range of challenges, such as improving catalog quality through large language models, enhancing machine translation, developing abuse prevention systems etc.
• Staying up-to-date with the latest advancements in machine learning and data science, and proactively identifying opportunities to apply these techniques to drive business impact.
• Contribute to the broader scientific community by publishing your work at top-tier conferences and journals.A day in the life

BASIC QUALIFICATIONS

- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience building machine learning models or developing algorithms for business application
- PhD or Master's degree in a technical field (e.g., computer science, statistics, applied mathematics) with 4+ years of relevant experience.
- Proven track record of designing, implementing, and deploying successful machine learning solutions for real-world business problems.
- Expertise in areas such as natural language processing, computer vision, deep learning, and other cutting-edge ML techniques.
- Strong programming skills in Python, R, or other relevant languages, and experience with popular ML frameworks like TensorFlow, PyTorch, or MXNet.
- Excellent communication and collaboration skills to effectively work with cross-functional teams.


PREFERRED QUALIFICATIONS

- Proficient in any one of these areas: large language models, NLP (Information retrieval, Machine Translation), Computer Vision, Classification models using Boosting/Bagging or Deep Neural Networks.