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Our team builds the intelligence layer that powers Microsoft’s next‑generation security detections—graph‑based reasoning, multi‑modal ML pipelines, campaign correlation, and threat‑centric analytics across the Defender ecosystem. As an Applied Scientist II , you will contribute hands‑on to the design, development, and deployment of ML and graph‑based algorithms that uncover sophisticated attacker behaviors and strengthen Microsoft’s disruptive security outcomes. This role is ideal for individuals who bring strong ML foundations, curiosity, and a desire to work in a deep‑technical, mission‑driven environment focused on protecting customers at global scale.
Job Responsibility:
Machine Learning & Modeling: Develop supervised and unsupervised ML models for anomaly detection, fraud/threat pattern discovery, alert classification, confidence scoring, and signal fidelity improvements
Build and maintain feature pipelines over multi-modal security telemetry (identity, endpoint, network, cloud)
Graph Reasoning & Analytics: Contribute to graph construction logic, schema evolution, and ontology-driven enrichment for Verdict Net, Verdict Propagation, Campaign Graphs, and Vortex insights
Implement graph traversal, multi-hop reasoning, and cluster detection algorithms to surface hidden attack patterns
Participate in performance optimization and health management of large-scale threat graphs
Data Engineering & Experimentation: Analyze large, noisy, high-dimensional security datasets using ADX/Kusto, Spark, and distributed compute platforms
Run A/B experiments, offline evaluations, and benchmark models to continually improve detection quality
Build high-quality research code and prototypes that transition smoothly to engineering teams for productionization
Cross-Functional Impact: Collaborate with detection engineering, threat research, product teams and red teams to integrate ML outcomes into real-world protection experiences
Translate complex analytical insights into actionable improvements for detections, disruptions, and customer-facing intelligence
Participate in on-call data issue triage (signal quality, false positives, enrichment gaps) as applicable for DEX workflows
Requirements:
Bachelor’s degree in CS, Data Science, EE, Mathematics or related field AND 4+ years of hands-on DS/ML experience OR Master’s degree AND 1+ years experience
Strong proficiency in Python, ML frameworks (PyTorch/TensorFlow), and data processing libraries
Experience with ML techniques such as: gradient-boosted models, supervised/unsupervised learning, embeddings, clustering, anomaly detection
Experience querying & analyzing large datasets using Kusto, SQL, Spark, or equivalent data engines
Strong fundamentals in probability, statistics, and algorithmic thinking
Ability to write clean, reliable research code and communicate findings clearly