GCS, Data Scientist, AVP - State Street

Job Title: GCS, Data Scientist

Who we are looking for

We are looking for a Data Scientist to support enterprise cybersecurity data science and analytics. This role will apply statistical modeling, machine learning, graph analytics, NLP, and GenAI techniques to large-scale security datasets to generate actionable insights, improve risk prioritization, enrich security operations, and help cybersecurity teams make faster, better-informed decisions. The ideal candidate combines strong data science depth with practical cybersecurity awareness and the ability to collaborate with security, engineering, governance, and risk stakeholders.

Why This Role is important to us

Cybersecurity teams increasingly rely on high-quality data, analytical models, and AI-enabled insights to prioritize risk, detect emerging issues, and respond effectively. This Data Scientist role strengthens the organization's ability to transform cybersecurity telemetry and operational data into predictive, explainable, and actionable intelligence. The role will help improve decision-making across security operations, risk management, vulnerability prioritization, threat detection, and enterprise cybersecurity reporting.

What you will be responsible for

As a Data Scientist, you will:

• Develop statistical, machine-learning, and AI-driven models that identify patterns, anomalies, relationships, and risk signals across enterprise cybersecurity datasets.
• Analyze large structured, semi-structured, graph, time-series, and text-based security datasets using Python, SQL, PySpark, and Databricks.
• Design and deliver analytics that support threat detection, vulnerability prioritization, incident enrichment, cyber risk scoring, and security posture measurement.
• Apply graph analytics and network science techniques to uncover relationships among identities, assets, vulnerabilities, applications, alerts, events, and threat indicators.
• Use NLP and GenAI techniques to summarize, classify, enrich, and operationalize cybersecurity data such as alerts, tickets, logs, findings, playbooks, and investigation notes.
• Build reusable analytical datasets, features, notebooks, models, dashboards, and model-monitoring outputs that can scale across enterprise security use cases.
• Partner with cybersecurity analysts, data engineers, platform engineers, architects, risk teams, and product owners to translate business and security needs into analytical solutions.
• Create Power BI reports and self-service dashboards that communicate model outputs, cyber trends, operational performance, and risk insights to technical and non-technical audiences.
• Support responsible model development practices, including validation, performance monitoring, explainability, privacy, security, lineage, and documentation in a regulated environment.