Head of AI

We are looking for a highly technical and strategic Head of AI to lead our machine learning department. This role is designed for a leader who excels in the science of model building—from advanced predictive modeling to reinforcement learning. You will be responsible for the entire AI lifecycle, ensuring our models provide a distinct competitive advantage. As a leader, you will mentor a team of experts, set rigorous research and engineering standards, and drive the core algorithmic innovation that powers our products.

Key Responsibilities

AI Roadmap & Strategy: Architect the company’s AI vision, focusing on solving complex business problems through advanced algorithmic solutions and robust model architectures.

Leadership & Team Growth: Lead and scale a high-performance Data Science team. Foster a culture of excellence, scientific rigor, and continuous learning.

End-to-End Model Ownership: Oversee the full lifecycle of proprietary models—from discovery and feature engineering to rigorous validation and production deployment.

Algorithmic Excellence: Drive the development of sophisticated models, with a particular focus on Reinforcement Learning and supervised/unsupervised learning at scale.

Frameworks & Standards: Establish the methodology for model evaluation, explainability (XAI), and monitoring to ensure high-performance and reliable decision-making in real-time.

Business Integration: Collaborate with executive leadership to identify high-impact opportunities where predictive modeling can transform product performance and ROI.

Requirements

Management Experience: 5+ years of experience leading Data Science teams, with a proven track record of managing Ph.D. and M.Sc. level researchers/engineers.

Advanced Model Building: 8+ years of hands-on experience developing complex, production-grade ML solutions (excluding CV/NLP focused roles).

Reinforcement Learning Expertise: Strong, demonstrable experience with Reinforcement Learning and decision-science frameworks.

Deep Technical Background: M.Sc. or Ph.D. in a quantitative field (CS, Math, Statistics, or Physics).

Engineering Proficiency: Expert-level Python skills and deep understanding of backend architecture, ensuring models are built for high-scale production.

Scientific Approach: Mastery of statistical analysis, feature engineering, and Explainable AI (XAI) techniques.

Strategic Thinker: Ability to align deep technical research with practical business goals and product delivery timelines.