GCS, Data Scientist, AVP

Job Title: GCS, Data Scientist Who we are looking for We are looking for a Data Scientist 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. Why This Role is important to us Cybersecurity teams increasingly rely on high-quality data, analytical models, and AI-enabled insights to prioritize risk, detect emerging issues, and respond effectively. This Data Scientist role strengthens the organization's ability to transform cybersecurity telemetry and operational data into predictive, explainable, and actionable intelligence. The role will help improve decision-making across security operations, risk management, vulnerability prioritization, threat detection, and enterprise cybersecurity reporting. 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. Education & Preferred Qualifications Minimum Qualifications 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 similarly 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. Strong written and verbal communication skills, including the ability to document analytical assumptions, model limitations, and recommended actions. Preferred Qualifications Master's degree or advanced coursework in Data Science, Computer Science, Statistics, Applied Mathematics, Cybersecurity, or a related field. 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. 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. Technical Skills Skill Area Expected Capabilities 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, executive-ready 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 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. 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. Ability to communicate complex analytical findings clearly to cybersecurity operators, engineers, risk stakeholders, and senior leaders. Collaborative working style with a bias for reusable solutions, documentation, operational discipline, and measurable business impact. Salary Range: $90,000 - $157,500 Annual The range quoted above applies to the role in the primary location specified. If the candidate would ultimately work outside of the primary location above,