Head of Data Science

Job description

About Peec AI AI search is changing how brands are discovered and chosen. People now ask ChatGPT, Gemini, Claude and Google’s AI answers what they used to search for, and AI assistants increasingly shape the journey from discovery to purchase. Winning customers now means winning over the AI systems that influence their decisions. Peec AI is an AI search platform that helps marketing teams understand and improve how their brands are discovered, represented, and recommended. We show them where they stand, what’s shaping it, and where to act. Since launching in January 2025, Peec AI has passed $20M ARR and 4,000 paying customers. We’re a Series A company backed by 20VC and Singular, with 100+ people across our Berlin HQ and our New York office. The role Almost everything Peec shows a customer passes through a model at some point. We track brands, products, sentiment, citations and sources across millions of AI answers. Those answers change whenever a provider ships a new model and differ by country, language and prompt. Turning that into numbers customers can trust and act on is the core data science problem at Peec. Our data science team has grown fast, with AI-powered features shipping across the product. We're now growing the team further and need someone to lead it. You'll turn a group of strong individual contributors into a high-powered, fast-shipping team. You'll report directly to the CTO and lead a team of 7 data scientists. What you'll do Leading people Hire, onboard and grow the data science team. We're hiring several data scientists right now, and you'll shape who joins and how they ramp up. Build a team culture that matches our values: a high bar for quality, full ownership of outcomes, and kindness towards each other Lead and inspire the team through regular 1:1s, actionable feedback and clear career paths Leading the technical work Set the technical direction and roadmap for data science, together with product and engineering leadership, and decide what the team works on and what it doesn't Own quality in production with strong operational excellence and reproducibility across the ML lifecycle Drive innovation by investing in scalable machine learning data infrastructure Stay close to the work: review designs and code, and step in hands-on when it matters Represent data science to leadership and customers What we're looking for 8+ years in data…