Head of AI & Customer Intelligence
Head of AI & Customer Intelligence
tap growth ai10-12 Years
- Posted 7 months ago
- Be among the first 10 applicants
Job Description
About The Role
This role leads the design and execution of AI-driven decisioning capabilities to enable personalized, data-led customer actions at scale. You will oversee advanced analytics, model governance, and decision orchestration to drive growth while ensuring ethical, compliant, and explainable AI adoption.
Key Responsibilities
This role leads the design and execution of AI-driven decisioning capabilities to enable personalized, data-led customer actions at scale. You will oversee advanced analytics, model governance, and decision orchestration to drive growth while ensuring ethical, compliant, and explainable AI adoption.
Key Responsibilities
- Own the end-to-end AI and decisioning portfolio, including use-case prioritization, model development, deployment, and lifecycle management.
- Design scalable decisioning architectures covering real-time and batch decision engines, rules frameworks, experimentation, and feature management.
- Govern model risk, ethics, explainability, and regulatory compliance, including bias testing and human-in-the-loop controls.
- Drive continuous performance uplift through test-and-learn, champion–challenger models, and causal measurement.
- Partner closely with data science, marketing, technology, and platform teams to embed AI into customer journeys and operations.
- Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, or related fields; Master's preferred.
- Minimum 10 years of experience in AI/ML, advanced analytics, or decisioning platforms.
- Strong understanding of ML engineering, model governance, and production-grade AI systems.
- Experience with cloud platforms (AWS, GCP, Azure) and large-scale data environments.
- Strong leadership, strategic thinking, and ability to translate AI into business impact.
More Info
Key Skills
explainability
causal measurement
human-in-the-loop controls
large-scale data environments
model governance
decision orchestration
rules frameworks
batch decision engines
feature management
bias testing
model risk ethics
AI-driven decisioning
