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Strong global brand and metro presence, but senior, niche cybersecurity-ML focus reduces competition.
Highly specialized cybersecurity ML skills and regulated financial context reduce cross-industry transferability.
Explicit 12–15 years plus deep ML, cybersecurity, and regulated-environment requirements make filters highly stringent.
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Develop and deploy statistical, machine learning, and AI models to identify risks, anomalies, and patterns in enterprise cybersecurity data.
Analyze diverse security datasets (including graph, time-series, text) using Python, SQL, PySpark, and Databricks to enhance threat detection and cyber risk scoring.
Partner with cybersecurity and engineering teams to build scalable analytical products, dashboards, and ensure responsible model governance in a regulated environment.
Master's degree or advanced coursework in Data Science, Computer Science, Statistics, Applied Mathematics, Cybersecurity, or related field.
12 to 15 years of experience in Data Analytics, Data Science, and Machine Learning with a strong background in statistical modeling.
Proficiency in Python, SQL, PySpark, Databricks, and experience developing large-scale data pipelines and distributed data-processing solutions.
Experience with cybersecurity concepts, tools (SIEM/SOAR), and cloud-native architectures (AWS), plus strong communication and stakeholder collaboration skills.
Experienced at integrating advanced analytics (graph analytics, NLP, GenAI) within cybersecurity environments and tools for operational risk insights.
Able to balance exploratory data science with production-grade analytical delivery and communicate effectively with technical and non-technical stakeholders.
Comfortable working in regulated financial services or similarly governed environments demanding security, privacy, and audit compliance.