Teams at Prevalent AI

The VP of Data Science will define and execute the company’s AI strategy, transform cybersecurity products into AI-first platforms, and lead development of production-grade machine learning capabilities. Responsibilities include building data science teams, establishing MLOps and AI architecture standards, deploying autonomous and LLM-driven workflows, governing responsible AI use, partnering across Product and Engineering, and representing the company through client engagements, research, publications, and industry thought leadership.

Leads client data engineering projects from requirements through production. Designs scalable Spark pipelines, diagnoses distributed job performance issues, tunes workloads to SLAs, and establishes storage and data layout strategies. Builds reusable Python frameworks, reviews code, mentors engineers, and supports containerization, object storage, CI/CD, observability, and operational documentation. The role requires deep PySpark and Spark internals expertise, large-scale time-series processing, Delta Lake or Iceberg, self-managed clusters, and hands-on coding.

Own the strategy, roadmap, growth, and lifecycle delivery of an Exposure Management application. Translate cybersecurity, vulnerability, identity, attack-path, and control data into actionable product capabilities using AI and knowledge graphs. Partner with clients, Sales, Marketing, Engineering, and Client Solutions to prioritize features, support go-to-market activities, and drive cross-sell. Lead and develop Product Owners while advancing agile, client-focused product practices.