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sennder is Europe's leading digital freight forwarder, redefining road logistics through a unified platform that powers the entire business. The company is transitioning from growth-through-acquisition to an outcome-driven, AI-native organization. As Head of Data, you will lead sennder's full Data & ML organization, including Analytics Engineering, Data Insights, Data & AI Platform, and Machine Learning & Data Science teams. You report to the CPTO and operate as a strategic partner to the incoming SVP of Engineering.
This is a hands-on, high-ownership builder role focused on turning data and AI capability into a competitive engine. Your core responsibilities include: (1) leading the Data & ML organization end-to-end, managing a Director of Engineering and stabilizing the leadership bench through coaching, hiring, and organizational design; (2) establishing a single source of truth for definitions, models, and platforms, retiring legacy dashboards and duplicate reporting; (3) making data quality a business issue by diagnosing root causes and building business cases for stakeholders; (4) commercializing ML and AI by translating technical work into margin expansion, cost reduction, and service differentiation; and (5) shifting the business perception of data from reporting support to strategic enabler through delivered impact.
You will own sennder's core unfair advantage—its data—and leverage the closed feedback loop between platform, operations, and data that competitors cannot replicate. The role requires building strong leadership benches, multiplying impact through managers rather than individual execution, and holding firm on technical standards while managing stakeholder change deliberately.
Required qualifications include 10+ years in data, analytics, or ML leadership with 5+ years leading multi-team organizations at meaningful scale. You need strong architectural understanding of modern data platforms and semantic layers, deep expertise in Data Product Management and business alignment, and working fluency in applied ML/AI sufficient to sponsor and commercialize initiatives. Experience with data governance and quality frameworks suited to AI-native businesses is essential. You must excel at making complex technical topics simple for non-technical executives, driving real change with stakeholders, and balancing technical rigor with collaborative implementation.
