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Senior Applied Data Scientist

India, Hyderabad · Job Posted March 20, 2026
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Job Description

Our team builds the intelligence layer that powers Microsoft’s next-generation threat detection ecosystem—spanning Vortex, Threat Graph, Verdict Net, and campaign-correlation workflows. We combine deep applied science, graph-theoretic reasoning, large-scale machine-learning, and multi-modal security analytics to uncover hidden attack patterns across identity, endpoint, network, and cloud. As part of a multidisciplinary organization, we design graph algorithms, develop ML models, operationalize high-confidence security signals, and partner closely with detection engineering to translate research into customer-impacting protections. Our work drives core advancements in attack-path discovery, anomaly detection, graph construction, and threat-hunting experiences across Microsoft Security.

Job Responsibility

  • Design, train, and deploy supervised/unsupervised ML models for: anomaly detection
  • attack pattern discovery
  • similarity scoring
  • Build ML pipelines that operate on large-scale, heterogeneous security telemetry
  • Develop graph embeddings, GNN models, clustering, and temporal sequence models to detect emerging threats
  • Build and optimize graph traversal algorithms for multi-hop attack path discovery
  • Correlate signals across identity, endpoint, network, and cloud domains
  • Analyze entities, edges, and temporal relationships to surface hidden attacker behaviors
  • Design/optimize graph schemas, ontologies, and semantic layers for threat detection
  • Work with graph-native DBs and query languages (e.g., GQL, ADX/Kusto)
  • Partner with infra teams to scale graph workloads across customer data
  • Stay current with academic research and convert novel ML/graph techniques into practical security applications
  • Run experimentation cycles (A/B tests, offline evaluation, model validation) to optimize detection precision/recall
  • Discover new attack patterns using clustering, community detection, and probabilistic methods
  • Partner with detection engineering, red teaming, and product teams to integrate ML/graph intelligence into protections
  • Translate complex graph/ML insights into actionable detection logic and SOC-ready intelligence
  • Communicate findings to security architects and leadership through visualizations, dashboards, and well-structured narratives

Requirements

  • 7+ years of hands-on experience in applied ML, data science, or security analytics
  • Strong expertise in one or more of: Graph algorithms, graph databases, GNNs
  • Large-scale ML pipelines
  • Unsupervised/behavioral anomaly detection
  • Statistical modeling, clustering, embeddings
  • Deep proficiency in Python, PyTorch/TensorFlow, and data processing frameworks
  • Experience working with large-scale telemetry (security logs, identity signals, network events, etc.)
  • Experience with distributed data systems and query languages (ADX/KQL, Spark, or similar)
  • Strong problem-solving skills with ability to work on ambiguous research problems

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