Data Architect & AI Engineering Lead, Cybersecurity - Assistant Vice President

As a Data Engineer / Scientist, you will:

·       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.

These skills will help you succeed in this role:

·       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.

Minimum Qualifications

·       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.

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.

Skill Area

Expected Capabilities

Data Engineering

Python, SQL, PySpark, Databricks, ETL/ELT, Lakehouse, Delta Lake, data modeling, orchestration, data quality, lineage and observability

Data Science / ML

Statistical modeling, feature engineering, anomaly detection, classification, clustering, experimentation, model evaluation and explainability.

AI & Gen AI

LLMs, NLP, RAG, embeddings, vector/semantic search, prompt engineering and AI-assisted workflows.

Cloud & DevOps

AWS, cloud data services, CI/CD, Git, automated testing, scalable processing and MLOps.

Cybersecurity Analytics

SIEM/SOAR workflows, alert enrichment, cyber telemetry, vulnerability data, identity risk, incident and response datasets.

Visualization & BI

Power BI dashboards, operational metrics, executive-ready reporting, trend analysis, self-service analytical products.

·   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.

State Street is one of the world's leading providers of financial services to institutional investors, including investment servicing, investment management, investment research, and trading. Technology and cybersecurity are critical enablers of our ability to safeguard information, support clients, and operate at enterprise scale.

We value collaboration, innovation, inclusion, and responsible technology. We are looking for technical talent who can solve meaningful problems, communicate clearly, and help build secure, scalable, data-driven capabilities for the future.

State Street is an equal opportunity and affirmative action employer.

Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.

We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.

As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.

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