GCS, Data Scientist, AVP

What you'll do

• Develop machine learning and AI models to identify patterns, anomalies, and risk signals in cybersecurity datasets.
• Analyze large structured, semi-structured, graph, and time-series datasets using Python, SQL, PySpark, and Databricks.
• Design analytics to support threat detection, vulnerability prioritization, incident enrichment, and cyber risk scoring.
• Apply graph analytics and network science to uncover relationships between identities, assets, and threat indicators.
• Use NLP and GenAI techniques to summarize, classify, and operationalize alerts, logs, and investigation notes.
• Build reusable analytical datasets, features, notebooks, and dashboards that scale across enterprise security use cases.
• Create Power BI reports to communicate model outputs, cyber trends, and operational performance to various audiences.
• Support responsible model development including validation, performance monitoring, explainability, and privacy in a regulated environment.

What we're looking for

• 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 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.
• Master's degree or advanced coursework in Data Science, Computer Science, Statistics, Applied Mathematics, Cybersecurity, or a related field (preferred).
• Experience operationalizing ML or AI solutions through feature pipelines, model monitoring, MLOps practices, reproducible notebooks, or production analytical workflows (preferred).
• Experience using graph frameworks, graph databases, knowledge graphs, entity resolution, embeddings, or relationship-based analytics for security or risk use cases (preferred).
• Experience applying NLP or GenAI to summarize, classify, extract, or enrich cybersecurity information from unstructured or semi-structured sources (preferred).
• Experience working in regulated, financial services, or enterprise-scale technology environments where security, governance, privacy, and auditability are important (preferred).
• Relevant certifications or training such as Security+, CySA+, GIAC, CISSP, AWS, Databricks, or machine-learning certifications (preferred).