Data Scientist , OpsTech Infra Eng.

Amazon is seeking a highly analytical and skilled Data Scientist to join our OpsTech Infrastructure Engineering (OTIE) team. The vision for our organization is to be the invisible scaffolding to provide Amazon’s network and device infrastructure for Global Operations. We deliver flexible, low-touch, cost-efficient infrastructure products by leveraging data, analytics, and automation to build a highly scalable and accessible network. If you are passionate about working with big data and thrive in a collaborative, innovative environment, we want to hear from you.

As a Data Scientist, you will be responsible for data exploration and analyses, as well as AI model development. You will collaborate with data engineers to collect, preprocess, and maintain high-quality datasets. You will dive deep into the available data, identifying trends, patterns, and insights to inform AI initiatives. You will design, develop, and implement AI models, including machine learning and deep learning algorithms, to solve complex business challenges, ensuring that these models are optimized for accuracy, scalability, and real-time performance. You will support the deployment of AI models into production environments, ensuring efficient and reliable operation, and own the model performance monitoring, make improvements, and implement retraining strategies. Strong business and communication skills are essential for collaborating with business owners to develop key business questions and build solutions that provide answers and drive change.

Key job responsibilities
Thinking Big and generating ideas with the stakeholders.
Working with customers and cross-functional stakeholder teams to identify, disambiguate, and define problems.
Scoping long-term solutions as a series of smaller, more manageable iterations.
Creating data science architectures, and building scalable solutions along with the data engineers.
Running simulations, measuring performance, building ML models and designing optimization algorithms.
Supporting existing models, while thinking about next generation solutions.
Keep up-to-date with the latest AI research, technologies, and industry best practices.
Share knowledge and promote AI innovation within the team.