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This listing doesn't post a salary. Most similar roles pay
$141,425–$203,900.
Based on 240 similar postings.
Employer
About State Street
State Street Corporation is one of the world''s largest custodian banks and asset managers, providing investment servicing, investment management, and investment research to institutional investors. Industry: Financial Services & Asset Custody
State Street currently has
351 open roles
on FindRole.
Listed pay typically runs
$115,000–$195,944
across 199 roles with salary data.
JOB TITLE: GCS, Data Scientist, AVP
The GCS, Data Scientist, AVP joins the enterprise cybersecurity team to transform telemetry and operational data into actionable intelligence. This role involves developing statistical, machine-learning, and AI-driven models to identify patterns, anomalies, and risk signals across large-scale security datasets. You will build reusable analytical datasets, features, and dashboards to support threat detection, vulnerability prioritization, and incident enrichment. Key responsibilities include applying graph analytics, NLP, and GenAI techniques to summarize and classify alerts, logs, and investigation notes. You will utilize Python, SQL, PySpark, and Databricks within AWS environments to deliver insights. The role solves critical business problems by improving risk prioritization, security posture measurement, and automated response workflows within a regulated environment, requiring close collaboration with security, engineering, and risk stakeholders.
What does a Data Scientist earn?
Median $173200 from 338 postings across 66 companies.
Develop machine learning and AI models to identify patterns, anomalies, and risk signals in cybersecurity datasets.
Analyze large structured, semi-structured, graph, and time-series datasets using Python, SQL, PySpark, and Databricks.
Design analytics to support threat detection, vulnerability prioritization, incident enrichment, and cyber risk scoring.
Apply graph analytics and network science to uncover relationships between identities, assets, and threat indicators.
Use NLP and GenAI techniques to summarize, classify, and operationalize alerts, logs, and investigation notes.
Build reusable analytical datasets, features, notebooks, and dashboards that scale across enterprise security use cases.
Create Power BI reports to communicate model outputs, cyber trends, and operational performance to various audiences.
Support responsible model development including validation, performance monitoring, explainability, and privacy in a regulated environment.
What we're looking for
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, analytics, or machine learning techniques to cybersecurity, risk, fraud, infrastructure, identity, or complex enterprise datasets.
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Cybersecurity, Information Systems, or equivalent practical experience.
Strong hands-on experience with Python and SQL for analytical modeling, exploratory analysis, statistical evaluation, and data manipulation.
Experience using PySpark and Databricks to process, analyze, and model large-scale datasets.
Experience creating clear, actionable dashboards and visualizations using Power BI or similar business intelligence tools.
Working knowledge of AWS data and analytics services or cloud-based analytical environments.
Familiarity with SIEM/SOAR platforms and the security workflows they support, including alert enrichment, detection analytics, triage, response, and reporting.
Experience with one or more advanced analytical methods such as graph analytics, NLP, GenAI, anomaly detection, classification, clustering, forecasting, or recommendation techniques.
Master's degree or advanced coursework in Data Science, Computer Science, Statistics, Applied Mathematics, Cybersecurity, or a related field (preferred).
Experience operationalizing ML or AI solutions through feature pipelines, model monitoring, MLOps practices, reproducible notebooks, or production analytical workflows (preferred).
Experience using graph frameworks, graph databases, knowledge graphs, entity resolution, embeddings, or relationship-based analytics for security or risk use cases (preferred).
Experience applying NLP or GenAI to summarize, classify, extract, or enrich cybersecurity information from unstructured or semi-structured sources (preferred).
Experience working in regulated, financial services, or enterprise-scale technology environments where security, governance, privacy, and auditability are important (preferred).
Relevant certifications or training such as Security+, CySA+, GIAC, CISSP, AWS, Databricks, or machine-learning certifications (preferred).
Data Science
Python
SQL
Causal Inference
Experimentation
ETL
Uplift Modeling
Synthetic Control
Difference-in-Differences
Data Architecture
AI agent s
Statistics
Econometrics
Python
SQL
Machine Learning
Generative AI
Large Language Models
Google Cloud Platform
Microsoft Azure
MLOps
Data Pipelines
APIs
Version Control
Software Development Lifecycle
Statistics
Model Monitoring