VP Data Science

Role purpose 
Prevalent AI is enhancing its established data science capability and is now looking for a visionary leader to 
transform our products into AI-first security platforms. The VP of Data Science will drive innovation, leverage 
modern AI/ML advancements, and embed intelligent automation across our Data Fabric and Exposure 
Management products —progressively reducing human intervention and increasing autonomous decision
making. This role will set the strategic direction, scale our data science function, and work closely with Product, 
Engineering and Managed Services team to deliver high-impact, production-grade AI capabilities that 
differentiate Prevalent AI in the market.  
This position demands a deep technical expert with a profound understanding of applied AI/ML, data science 
methodologies, and scalable cloud-native architectures. The VP of Data Science will play a pivotal role in 
shaping product strategy and accelerating time to value for clients.

Key accountabilities  

Establish a Clear, Long-Term AI Vision for Prevalent AI 
Articulate how AI transforms: 

• Customer experience 
• Product differentiation 
• Operational efficiency 
• Scalability

Transform Our Products Into AI-First Systems 

Transform Prevalent AI’s products into AI-first platforms by embedding intelligence across workflows 
and eliminating manual steps in analysis, configuration, triage, prioritisation, and reporting. 

• Define an AI-first product architecture that enables automated detection, reasoning, 
  enrichment, and decision-making across all modules. 
• Identify high-friction, human-heavy workflows and redesign them for ML-driven automation 
  including exposure and risk scoring frameworks, prioritisation and ranking models across noisy 
  and conflicting data sources, and strategies to reduce false positives and analyst fatigue.  
• Partner with Engineering to build shared AI infrastructure such as feature stores, model 
  serving layers, inference pipelines, and prompt/orchestration frameworks. 
• Introduce autonomous agents or LLM-driven components for summarisation, rule generation, 
  query creation, or automated investigation playbooks.

Govern the use of GenAI and LLMs by evaluating various trade-offs like accuracy, explainability, 
cost, latency, determinism, and customer trust—ensuring that AI-driven automation is applied 
judiciously in critical workflows. 

• Establish continuous learning pipelines so models improve with real-world feedback while 
  maintaining guardrails and robustness. 
• Implement explainability and transparency mechanisms so AI-driven decisions can be 
  understood, trusted, and audited by customers, internal and third-party teams. 
• Define and track clear success metrics for AI/ML initiatives, linking model performance to 
  measurable customer outcomes such as improved prioritisation accuracy, reduced remediation 
  time, reduced false positives, and increased operational efficiency.

Leadership and Team building: 

• Build, mentor, and lead a high-performing and diverse team of data scientists and foster a culture of 
  technical excellence, scientific rigor, collaboration, and continuous learning. 
• Establish best practices for model development, experimental design, validation, deployment, and 
  monitoring, ensuring the highest standards of quality and reliability. 
• Attract, recruit, and retain top-tier data science talent, positioning Prevalent AI as an employer of choice 
  in the AI/ML community.

 
 
Cross functional collaboration: 

• Partner closely with Product Management to translate business requirements into AI/ML feature 
  specifications, ensuring that data science initiatives directly address market needs and deliver 
  measurable client value. 
• Collaborate with Engineering teams to ensure seamless integration, scalability, and robust deployment 
  of AI/ML solutions within the platform architecture. 
• Work with Client Solutions and Sales teams to articulate the value proposition of AI-driven insights, 
  support client engagements, and accelerate client time to value through efficient onboarding and rapid 
  insight delivery. 
• Actively contribute to the overall technology strategy and architectural decisions in collaboration with 
  the CTO.

 
 
Thought leadership: 

• Lead internal research initiatives to explore new frontiers in cybersecurity AI, publish findings, and 
  contribute to the broader scientific community with clear accountability for translating insights into 
  production-grade, customer-impacting capabilities. Engage directly with strategic clients to showcase 
  our Data Science, AI/ML capabilities through our products and demonstrate measurable impact.   
• Represent Prevalent AI as a recognized thought leader in AI/ML and cybersecurity through 
  presentations at industry conferences, publications, and active participation in academic and research 
  communities, as well as fostering relationships with the investor community, to enhance brand 
  recognition, attract talent, and drive business growth

 
 
Skills and Experience 

• Relevant and recent experience in driving AI first implementation for data driven software solutions 

• Extensive experience in data science and machine learning, with a significant portion in senior 
  leadership roles (e.g., VP of Data Science, Head of AI/ML)

• Proven track record of defining and executing AI/ML strategy, building and scaling data science teams, 
  and delivering production-ready, impactful AI solutions in a growth-stage environment. 

• Comfortable challenging teams on modelling choices and assumptions, and ensuring the appropriate 
  use of classical data science and machine learning versus GenAI, rather than defaulting to GenAI for 
  all use cases 

• Strong architectural vision for AI/ML systems and experience with MLOps best practices for deploying 
  and managing models at scale. 

• Demonstrated experience setting direction for AI/ML systems built on large-scale data platforms, 
  including the use of graph-based analytics and machine learning to derive contextual, relationship
  aware insights from complex and interconnected data. 

• Excellent communication, presentation, and stakeholder management skills, with the ability to 
  articulate complex technical concepts to diverse audiences, from technical teams to C-level executives 
  and investors. 

• Strong strategic thinking, problem-solving, and decision-making capabilities.