Role intent Live

Applied Data Scientist II

Microsoft

Work Mode

Onsite

Employment Type

FULL TIME

Location

India, Telangana, Hyderabad, India, Karnataka, Bangalore

Application Deadline

September 21, 2026

Apply graph‑focused ML techniques (graph embeddings, GNNs, similarity scoring, relationship modeling).

Responsibilities

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). 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. Analyze large, noisy, high‑dimensional security datasets using ADX/Kusto, Spark, and distributed compute platforms. Build high-quality research code and prototypes that transition smoothly to engineering teams for productionization. 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.

Required Qualifications

Bachelor's degree in CS, Data Science, EE, Mathematics or related field AND 4+ years of hands-on DS/ML 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.

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