Microsoft's Cloud & AI organization is seeking to improve the security posture of its products and services by leveraging data science and machine learning. The MSRC Data Science team aims to build data pipelines, perform data mining, develop ML models, and generate insights from security-related data to address customer scenarios and drive a data-driven culture within security.
Requirements
- 3+ years’ experience in building data pipelines using cloud computing like, Kusto (Azure Data Explorer), Azure ML, Azure Key Vault, Azure Storage or similar.
- Experience in solving data science problems in Cybersecurity.
- Experience using technologies such as Big Data platforms
- Experience in DevOps and building end to end pipelines using CI/CD.
- Ability to meet Microsoft, customer and/or government security screening requirements are required for this role.
- Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
Responsibilities
- Data wrangling, data analysis and transforming large data sets to build scalable pipelines.
- Use Machine Leaning and Deep Learning techniques to build prediction models on structured and unstructured data.
- Improve the performance and run-time of ML models
- Keep up to speed with the current academic and industry advances in machine learning techniques, experiment with their application to improve our ML models.
- Work in collaboration with teammates to ensure reliable and trust-worthy data for critical business decisions to improve reliability, scalability, and efficiency.
- Build data processing tools, libraries, frameworks and performing and getting insights from data analysis Learning and using tools like Azure ML, Azure Data Factory, Python and more public and Microsoft internal tools
- Ensure the solutions are stable, supportable, extensible, and trusted by our business partners.
Other
- Working with the business to understand a real-world scenario and translate it into a machine learning problem and think of optimal ways of solving it.
- Performs documentation of work in progress, experimentation results, plans, etc.
- Collaborate with PMs to plan and prioritize ML deliverables as part of broader business initiatives, ensure delivery according to the planned schedule.
- Mentor early-in-career teammates and establish high standards in both data science and engineering excellence.
- Embody our culture and values
