VP AI

• Leading AI management: Overseeing the recruitment, onboarding, professional development, mentoring, and training of all researchers and data scientists in the unit.
• Formulating research vision and strategy: Defining the strategic research domains (e.g., NLP, Computer Vision, Reinforcement Learning) and identifying breakthrough technological opportunities.
• Driving research intuition for adoption/development: Leading early-stage decision-making in the AI model development process (model training, model selection, use of existing models, hybrid/classical approaches, etc.).
• Defining research methodologies: Establishing work processes for algorithm research and development, including experiment management, model evaluation, and internal peer-review procedures.
• Managing tech stakeholders: Collaborating with academia and industry partners, initiating joint projects, and bringing in innovative external knowledge.
• Translating business problems into research questions: Converting complex operational needs into solvable AI/research challenges and defining clear success metrics.
• Ensuring quality and excellence: Holding overall responsibility for the technical quality and innovation of models and algorithms developed across all squads.
• Advising management: Serving as a senior advisor to the executive team and CTO on AI trends, risks, and opportunities.

• Mandatory: Master's degree in a quantitative field (e.g., Computer Science, Electrical Engineering, Physics, Mathematics).
• Strong Advantage: Ph.D. in a core machine learning/artificial intelligence discipline.

• Mandatory:
• At least 8 years of hands-on experience in AI/ML research and development.
• At least 3 years of experience managing and leading AI researchers or data scientists.
• Proven experience in leading impactful research projects through to production.
• Deep familiarity and hands-on experience with multiple AI domains, with Computer Vision as a must, and others such as NLP, signal processing, time-series analysis, etc.
• Strong Advantage:
• Academic publications in top-tier conferences.

• Strong theoretical and practical understanding of machine learning and deep learning algorithms.
• Strong “AI sense” - intuition and ability to identify promising approaches for solving complex problems.
• Strategic and research-oriented mindset with the ability to build long-term research plans.
• Excellent communication skills and the ability to simplify complex concepts.
• Proven leadership and mentoring skills, capable of nurturing top-tier researchers.
• Deep familiarity with data-centric AI methodologies.

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