Senior Cybersecurity Data Scientist (AVP)

State Street

Quincy (MA)

On-site

USD 90,000 - 157,500

Full time

14 days+

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Benefits offered by this job

401K with company match

Health insurance

Dental and vision insurance

Paid time off

Employee Assistance Program

Incentive compensation

Job summary

State Street in Quincy, MA is seeking a GCS Data Scientist to advance cybersecurity data science, applying ML, NLP, graph analytics, and GenAI to large-scale security data to generate actionable insights and improve risk prioritization. You will collaborate with security, engineering, governance, and risk stakeholders to translate needs into analytical solutions.

The role emphasizes building reusable datasets, dashboards in Power BI, and responsible model development in a regulated environment,

Qualifications

5-8 years of total professional experience in data science, analytics, machine learning, data engineering, or related technical roles.
2-4 years of experience applying data science techniques to cybersecurity, risk, or enterprise datasets.
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Cybersecurity, Information Systems, or equivalent experience.

Responsibilities

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.
Continuously evaluate emerging analytics, ML, graph, NLP, GenAI, SIEM, SOAR, and cloud data capabilities for practical application to cybersecurity outcomes.