Data Scientist and Analyst - Assistant Vice President

State Street; Bengaluru; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

State Street is seeking a Data Scientist and Analyst to support enterprise cybersecurity data science and analytics. This role will apply statistical modeling, ML, graph analytics, NLP, and GenAI 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

Qualifications

• , 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.

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.

Skills

Statistical modeling

Data analytics

Machine learning

Python

SQL

PySpark

Databricks

Graph analytics

NLP

GenAI

Power BI

AWS

Stakeholder collaboration

Education

Master's degree in Data Science or related field

Tools

Databricks

Power BI

APIs

AWS

Job description

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