Principal Data & Applied Scientist

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<div class="isg-job-description“>OverviewThe CoreAI team is dedicated to empowering every developer to shape the future with AI. This involves creating tools, platforms, and workflows that redefine software development and enable dynamic, AI driven applications. The Core AI Infra data science team is focused on building telemetry and observability for the Microsoft Graphics Processing Units (GPU’s)
ResponsibilitiesCollaborates with others to identify opportunities to optimize data tools used to transform, manage, and access data across teams, scaling with efficiency and reduced time to new data insights. Writes code to test advanced storage and availability of data platforms and leads the implementation of sustainable design patterns to make data platforms more usable and robust to failure and change. Identifies trends across relevant data sources that allow others to develop insights to plan future data architecture designs or solution fixes. Uses and contributes to the development of advanced and reusable modification techniques and operations (e.g., inserting, aggregating, joining) to transform raw data into a form (e.g., dimensional data model) that is compatible with downstream data consumers, databases, and formats that support applications, analytics and reporting. Expands the Application and reusability of software, query languages, and computing tools (e.g., cloud-based) to transform raw data from multiple pipelines.. Identifies multiple, complex data sources and builds advanced code to extract raw data from identified upstream sources using query languages, tools, or machine learning algorithms, while assuring quality, scale, and reliability of the data across the pipeline. Contributes to the code review process by providing feedback and suggestions for implementation. Drives the business case to implement advanced orchestration techniques that automate data extraction logic for many sources of data. Plans and strategizes the use of advanced data protocols, reduction techniques, and aggregation approaches to validate the quality of extracted data across the data pipeline, consistent with the service level agreement (SLA). Refines and improves methods and tools used to track and maintain source control and assists in the development of versioning strategy. Applies deep knowledge of data to validate that the correct data is ingested and that the data is applied accurately across the pipeline. Creates data architectural documents for end-to-end systems.

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