Data Architect & AI Engineering Lead, Cybersecurity - Assistant Vice President
State Street · Posted Oct 14
Investment servicing, custody, asset management, and trading solutions for institutional investors
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About the role
State Street is a global provider of financial services to institutional investors, and its cybersecurity function depends on engineering, integrating, analyzing, and intelligently interpreting large volumes of complex security data. This AVP-level individual contributor role bridges data engineering, data science, cybersecurity, and AI, building trusted data foundations — Lakehouse platforms, data pipelines, ML/AI models, and GenAI solutions — from security telemetry. The role partners with cybersecurity, platform engineering, architecture, governance, and risk stakeholders to improve threat detection, risk prioritization, security operations, and enterprise decision-making.
What you will do
- Design and deliver scalable data pipelines, Lakehouse architectures, data models, and analytics-ready datasets using Python, SQL, PySpark, Databricks, and cloud technologies.
- Build reusable frameworks for data ingestion, transformation, aggregation, data quality, reconciliation, orchestration, lineage, and observability.
- Develop statistical, machine-learning, and AI models to identify patterns, anomalies, relationships, threats, and risk signals across cybersecurity datasets.
- Apply ML, graph analytics, NLP, and Generative AI to improve threat detection, vulnerability prioritization, incident enrichment, cyber-risk scoring, and security posture insights.
- Develop AI solutions leveraging LLMs, embeddings, semantic/vector search, RAG, and intelligent workflows to summarize, classify, correlate, and enrich cybersecurity information.
- Operationalize data science and AI solutions through feature pipelines, MLOps, CI/CD, automated testing, model monitoring, and production workflows.
- Engineer and analyze high-volume structured, semi-structured, time-series, graph, and unstructured security data across cloud and enterprise platforms.
- Build Power BI dashboards and self-service analytical products to communicate security trends, operational performance, risk insights, and model outcomes.
- Establish strong engineering and responsible AI practices covering performance, scalability, resiliency, explainability, security, privacy, lineage, governance, and auditability.
- Partner with cybersecurity, data and platform engineering, architecture, governance, risk, and product teams to translate complex security challenges into scalable data and AI solutions.
- Evaluate emerging data engineering, ML, GenAI, graph, cloud, SIEM/SOAR, and cybersecurity technologies and drive their practical adoption.
Skills used in this role
What the employer is looking for
- Strong technical expertise across Data Engineering, Data Science, Machine Learning, and AI/GenAI.
- Deep understanding of Lakehouse architectures, distributed data processing, ETL/ELT, data modeling, data quality, orchestration, and pipeline optimization.
- Hands-on expertise with Python, SQL, PySpark, Databricks, and cloud data platforms.
- Experience with statistical modeling, feature engineering, anomaly detection, supervised and unsupervised learning, experimentation, and model evaluation.
- Practical knowledge of LLMs, RAG, embeddings, vector search, NLP, graph analytics, and responsible AI practices.
- Understanding of cybersecurity concepts including security telemetry, threat detection, vulnerability management, identity risk, threat intelligence, incidents, and SIEM/SOAR workflows.
- Ability to lead complex technical initiatives, influence architecture and engineering decisions, and communicate effectively with technical teams and senior stakeholders.
- Strong focus on automation, reusable engineering patterns, operational excellence, governance, and measurable business outcomes.
- 15 to 20 years of professional experience across data engineering, data science, analytics, cloud, machine learning, AI, or related technology disciplines.
- Experience applying data engineering, analytics, ML, or AI techniques to cybersecurity, risk, fraud, identity, infrastructure, or other large-scale enterprise datasets.
- Bachelor's degree in computer science, Data Science, Statistics, Mathematics, Engineering, Cybersecurity, Information Systems, or equivalent experience.
- Strong hands-on experience with Python, SQL, PySpark, Databricks, ETL/ELT, distributed processing, and large-scale data engineering.
- Experience with Lakehouse/data platform architecture, data pipelines, modeling, data quality, monitoring, lineage, and performance optimization.
- Working knowledge of AWS or comparable cloud data and analytics environments.
- Experience operationalizing analytical and ML solutions using CI/CD, version control, testing, monitoring, and MLOps practices.
- Experience with Power BI or equivalent visualization technologies.
- Strong written, verbal, analytical, and stakeholder communication skills.
- This is an individual contributor role with strong cross-functional collaboration expectations.
- The role will require sound judgment when working with sensitive cybersecurity, risk, operational, and regulated data.
- The candidate should be comfortable balancing exploratory data science, production-minded analytical delivery, and stakeholder communication.
Preferred qualifications
- Master's degree or advanced coursework in Data Science, Computer Science, AI/ML, Statistics, Applied Mathematics, Cybersecurity, or related disciplines.
- Experience with Databricks/Delta Lake and Medallion architecture patterns.
- Experience with LLMs, RAG, embeddings, vector search, prompt engineering, NLP, knowledge graphs, entity resolution, or AI-assisted workflows.
- Experience developing production-grade ML/AI pipelines, model monitoring, feature pipelines, APIs, or MLOps solutions.
- Experience working with cybersecurity datasets such as endpoint telemetry, authentication events, cloud security logs, vulnerability data, asset inventories, network events, incidents, SIEM data, and threat intelligence.
- Experience in financial services, regulated industries, or enterprise environments with strong security, privacy, governance, and audit requirements.
- Relevant Security+, CySA+, GIAC, CISSP, AWS, Databricks, Data Engineering, ML/AI, or cybersecurity certifications are advantageous.
Benefits and support
- Flexible work-life support
- Paid volunteer days
- Inclusive development opportunities
- Vibrant employee networks
About State Street
State Street Corporation is a leading global financial services holding company providing institutional investors with investment servicing, investment management, and investment research and trading. Operating in more than 100 geographic markets worldwide, the company manages and administers trillions of dollars in assets through its core divisions like State Street Global Advisors and State Street Alpha. Headquartered in Boston, Massachusetts, it traces its origins back to 1792.
- Industry
- Financial Services
- Company size
- 51000+ employees
- Founded
- 1792-06-25
- Location
- Boston, Massachusetts, USA
- Funding stage
- Public Company
Leadership
Chairman and Chief Executive Officer
John Woods
Chief Financial Officer
Mostapha Tahiri
President of State Street Alpha & Chairman of Asia Pacific
Ann Fogarty
Enterprise Chief Operating Officer
President and Chief Executive Officer of State Street Investment Management
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