New 
Data and Applied Scientist
 Microsoft | |
 United States, Texas, Irving | |
| 7000 State Highway 161 (Show on map) | |
 Apr 05, 2025 | |
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OverviewSecurity represents the most critical priorities for our customers in a world awash in digital threats, regulatory scrutiny, and estate complexity. Microsoft Security aspires to make the world a safer place for all. We want to reshape security and empower every user, customer, and developer with a security cloud that protects them with end to end, simplified solutions. The Microsoft Security organization accelerates Microsoft's mission and bold ambitions to ensure that our company and industry is securing digital technology platforms, devices, and clouds in our customers' heterogeneous environments, as well as ensuring the security of our own internal estate. Our culture is centered on embracing a growth mindset, a theme of inspiring excellence, and encouraging teams and leaders to bring their best each day. In doing so, we create life-changing innovations that impact billions of lives around the world. Are you passionate about the idea of protecting over a billion people and making the world a durably safer place? The Microsoft Security Response Center (MSRC) is on the forefront of protecting the breadth of Microsoft's customers from emerging threats to security and privacy. The MSRC Data Science team is responsible in building data pipelines, reporting dashboards, data engineering, Machine Learning (ML) models and insights on security related data.We combine our data science work with business and engineering knowledge to provide unique insights into customer scenarios within Security. We are looking for a hands-on Data and Applied Scientist with experience to build reliable products and make business impact using Data Analysis, Data Engineering, Machine learning, Data Visualization, and more.We are looking for a Data and Applied Scientist to partner with a wide range of Security leads and PMs, Engineers, and Data Scientists, build and deliver scalable BI and AI solutions. Candidate should be able to build robust BI, ML/AI data pipelines, leverage our data and infrastructure to deliver valuable solutions for our customers. 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.In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.
ResponsibilitiesModel Development & Deployment:Collaborate with data scientists and engineers to design, build, and deploy machine learning models at scale.Develop and maintain data pipelnes using MLOps/AIOPs pipelines to automate the end-to-end lifecycle of machine learning models (from development to deployment, monitoring, and retraining).Build data pipelines, ETL using Kusto queries and DAX to build leadership level metrics, KPIs, trends and visualizations using Power BI dashboards.Work on the integration of models into production systems while ensuring scalability, security, and performance.Model Operationalization:Implement CI/CD pipelines for ML models, ensuring smooth deployments with minimal downtime.Design and deploy robust monitoring and alerting systems for ML models in production to detect issues such as model drift or data skew.Implement model governance, version control, and logging systems to ensure compliance with internal standards and external regulations.Optimization & Scalability:Optimize machine learning models and pipelines for performance and cost efficiency (compute, storage).Manage infrastructure for ML workloads using cloud-native tools (Azure, Kubernetes, Docker) or other container orchestration platforms.Collaboration & Communication:Partner with cross-functional teams, including Data Engineering, Product Management, and other Engineering teams to build cohesive solutions.Provide technical guidance to junior engineers and drive best practices for MLOps/AIOPS within the team.Security & Compliance:Work on securing models, data pipelines, and infrastructure in compliance with Microsoft's security standards.Ensure that the entire ML lifecycle adheres to privacy and compliance requirements (e.g., GDPR, CCPA). | |