Amazon
Sr. Applied Scientist, Amazon Devices
Amazon, Seattle, Washington, us, 98127
Job ID: 2802259 | Amazon.com Services LLCThe Sr. Applied Scientist will be responsible for building advanced forecasting models for Amazon Devices using the latest machine learning and deep learning techniques. They will develop scalable, accurate predictive models to drive key decision making that supports critical business decisions (including pricing, promotions and supply chain) for all of Amazon consumer hardware product lines WW. Collaborating with cross-functional teams, they will integrate these models into operational systems to enhance data-driven decision-making and optimize business outcomes. A strong focus on applying cutting-edge ML/DL methodologies and a passion for innovation will be key to success in this role.Key job responsibilities
Build and implement advanced forecasting models using machine learning techniques such as transfer learning, neural networks, and time series models, driving decisions for pricing, promotions, and supply chain optimization.Write efficient, scalable production code (Python or equivalent) to develop and productionalize models using frameworks like TensorFlow, PyTorch, and scikit-learn, collaborating with data engineers to ensure smooth deployment into real-time systems.Continuously optimize models through techniques like hyperparameter tuning, feature engineering, and cross-validation, leveraging tools such as TensorFlow and Keras to improve performance and scalability.Communicate complex model results and insights using data visualization tools (e.g., Matplotlib, Seaborn) and provide actionable recommendations to cross-functional teams for pricing, promotions, and supply chain strategies.BASIC QUALIFICATIONS
3+ years of building machine learning models for business application experiencePhD, or Master's degree and 6+ years of applied research experienceExperience programming in Java, C++, Python or related languageExperience with neural deep learning methods and machine learningPREFERRED QUALIFICATIONS
Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.Experience with large scale distributed systems such as Hadoop, Spark etc.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
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Build and implement advanced forecasting models using machine learning techniques such as transfer learning, neural networks, and time series models, driving decisions for pricing, promotions, and supply chain optimization.Write efficient, scalable production code (Python or equivalent) to develop and productionalize models using frameworks like TensorFlow, PyTorch, and scikit-learn, collaborating with data engineers to ensure smooth deployment into real-time systems.Continuously optimize models through techniques like hyperparameter tuning, feature engineering, and cross-validation, leveraging tools such as TensorFlow and Keras to improve performance and scalability.Communicate complex model results and insights using data visualization tools (e.g., Matplotlib, Seaborn) and provide actionable recommendations to cross-functional teams for pricing, promotions, and supply chain strategies.BASIC QUALIFICATIONS
3+ years of building machine learning models for business application experiencePhD, or Master's degree and 6+ years of applied research experienceExperience programming in Java, C++, Python or related languageExperience with neural deep learning methods and machine learningPREFERRED QUALIFICATIONS
Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.Experience with large scale distributed systems such as Hadoop, Spark etc.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
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