Who we are looking for:

We are looking for a Data Scientist and Analyst 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.

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

What we value:

These skills will help you succeed in this role:

Strong analytical judgment, intellectual curiosity, and the ability to frame ambiguous cybersecurity problems as measurable data science opportunities.
Hands-on data science capability, including feature engineering, model development, statistical analysis, experimentation, and model performance evaluation.
Practical understanding of cybersecurity concepts, including threat detection, vulnerabilities, identity and access risk, cyber incidents, SIEM/SOAR workflows, and security telemetry.

Collaborative working style with a bias for reusable solutions, documentation, operational discipline, and measurable business impact.

Mandatory skills / experience:

Master's degree or advanced coursework in Data Science, Computer Science, Statistics, Applied Mathematics, Cybersecurity, or a related field.
12 to 15 years of relevant experience in Data Analytics, Data Science and Machine Learning with statistical modelling background.
Strong experience developing large-scale data pipelines and distributed data-processing solutions.
Experience with Python, SQL, APIs, workflow automation, and cloud-native architectures.
Strong communication and stakeholder collaboration skills.

Data Science / ML: Statistical modeling, supervised and unsupervised learning, feature engineering, model evaluation, anomaly detection, experimentation, explainability.

Programming & Data: Python, SQL, PySpark, Databricks notebooks/jobs, scalable data preparation, analytical datasets, reusable feature pipelines.

Visualization & BI: Power BI dashboards, operational metrics, executiveready reporting, trend analysis, self-service analytical products.

Cloud & Platforms: AWS analytical environments, cloud data services, secure data handling, scalable batch and interactive analytics.

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

Advanced Analytics: Graph analytics, NLP, GenAI, relationship analytics, entity resolution, text extraction, summarization, classification, and enrichment.

Additional requirements / Good to have skills: Preferred Qualifications

Experience operationalizing ML or AI solutions through feature pipelines, model monitoring, MLOps practices, reproducible notebooks, or production analytical workflows.
Experience using graph frameworks, graph databases, knowledge graphs, entity resolution, embeddings, or relationship-based analytics for security or risk use cases.
Experience applying NLP or GenAI to summarize, classify, extract, or enrich cybersecurity information from unstructured or semi-structured sources.
LangGraph, Semantic Kernel,CrewAI,AutoGen,LangChain, or similar frameworks.
Databricks, Snowflake, Spark, Kafka, Delta Lake, Iceberg, and Airflow.
Experience with vector databases, semantic search, and enterprise RAG platforms.
Experience implementingMLOps,LLMOps, AI observability, and evaluation frameworks.
Knowledge of Responsible AI, data governance, and model risk management.
Familiarity with cybersecurity data sources such as endpoint telemetry, authentication logs, cloud security events, vulnerability findings, asset inventories, application records, network events, incident tickets, or threat intelligence.
Experience working in regulated financial services, or enterprise-scale technology environments where security, governance, privacy, and auditability are important.
Relevant certifications or training such as Security+, CySA+, GIAC, CISSP, AWS, Databricks, or machine-learning certifications are helpful but not required.

Mode of work:

Hydrid

Additional information (if any):

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.

About State Street

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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