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Strong brand, metro location and broad skills present, but seniority and cybersecurity specialization temper competition.
Highly specialized cybersecurity ML role requiring domain experience, reducing cross-industry transferability.
Explicit 12-15 years plus mandatory ML, cybersecurity, cloud, and tooling experience increases shortlisting strictness.
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Develop and operationalize advanced statistical, machine learning, and AI models on large-scale cybersecurity datasets to improve threat detection, risk prioritization, and security operations.
Design and deliver analytic products including feature pipelines, dashboards, and reports for technical and non-technical stakeholders in a regulated financial services environment.
Collaborate cross-functionally with cybersecurity, engineering, risk, and data teams to translate security challenges into scalable analytic solutions using tools like Python, SQL, PySpark, and cloud platforms.
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 strong statistical modeling background.
Proficiency in Python, SQL, PySpark, Databricks, and experience with large-scale distributed data processing and cloud-native architectures (AWS preferred).
Experience with cybersecurity data and tools including SIEM/SOAR workflows, cyber telemetry, vulnerability data, and incident response datasets.
Deep expertise combining data science/ML and practical cybersecurity domain knowledge, especially in threat detection and risk scoring.
Experience delivering production-ready AI/ML solutions in highly regulated, enterprise-scale environments with disciplined model governance and explainability.
Strong collaborator comfortable working cross-functionally to build reusable analytic solutions and communicate complex insights to both technical and non-technical stakeholders.