Principal Security Data Scientist

The Sovereign Cloud Security Health team is looking for a Data Scientist to help secure Microsoft’s clouds through innovative, cloud-native security solutions. We operate internal security services that power scanning, monitoring, alerting, and detection at scale, with a strong focus on sovereign cloud environments. In this role, you’ll solve complex security and systems challenges in a fast-paced, agile setting. You’ll work closely with Azure Security engineering teams, product managers, and internal stakeholders to deliver resilient solutions. If you’re passionate about building scalable, distributed, and highly available services, we’d love to have you on the team.    

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. 

Defines business-strategy goals, customer-strategy goals, and solution-strategy goals. The Principal Security Data Scientist will partner with teams to identify and explore opportunities for the application of machine learning (ML) and other data-science tools. Leverages technical expertise to develop partnerships between product teams, Sales teams, Area teams, and Services. Work collaboratively across disciplines. Leads involvement of intellectual property (IP) definition improvement. Coaches and mentors less expPrincipal Security Data Scientisterienced engineers.

Business Understanding and Impact

Leverages subject matter expertise to analyze problems and issues facing projects to uncover, manage, and/or mitigate factors that can influence final outcomes across product lines. Partners with business team to drive strategy and recommend improvements. Raises opportunities to look for new work opportunities and different contexts to use existing work. Establishes, applies, and teaches standards and best practices.

Coding and Debugging

Independently writes efficient, readable, extensible code/model that spans multiple features/solutions. Contributes to the code/model review process by providing feedback and suggestions for implementation and improvement. Develops expertise in proper modeling, coding, and/or debugging techniques such as locating, isolating, and resolving errors and/or defects. Leads a project team in the gathering, integrating, and interpreting of data/information from multiple sources in order to properly troubleshoot errors. Provides feedback on non-optimized features/solutions back to product group, and explores potential for new features. Leverages expert-level proficiency of big-data software engineering concepts, such as Hadoop Ecosystem, Apache Spark, continuous integration and continuous delivery (CI/CD), Docker, Delta Lake, MLflow, AML, and representational state transfer (REST) application programming interface (API) consumption/development.

Customer/Partner Orientation

Commits to a customer-oriented focus by acknowledging customer needs and perspectives, validating customer perspectives, focusing on broader customer organization/context, and serving as a trusted advisor. Identifies opportunities and adds valuable insight by incorporating an understanding of the business, product/service functionality, data sources, methodologies to reframe problems, and the customer perspective. Interprets results, develops insights, and effectively communicates results to the customer. Leads the discussion with customers and offers pragmatic solutions that are feasible given their data limitations.

Data Preparation and Understanding

Oversees data acquisition efforts and ensures data is properly formatted and accurately described. Utilizes key technologies and tools necessary for data exploration (e.g., structured query language [SQL], Python). Uses querying, visualization, and reporting techniques to explore the data, including distribution of key attributes, relationships between attributes, simple aggregations, properties of significant sub-populations, and statistical analyses. Mentors and coaches engineers in data cleaning and analysis best practices. Identifies gaps in current data sets and drives onboarding of new data sets (e.g., bringing on third-party data sets). Drives discussions around ethics and privacy policies related to collecting and preparing data. Integrates industry-wide ethics insights and best practices to influence internal processes and drive decision-