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Sennder is Europe's leading digital freight forwarder, redefining road logistics through a unified platform powered by AI. The company is transitioning from growth-through-acquisition to an outcome-driven, AI-native organization. As the Engineering Manager for Machine Learning & Data Science, you will lead a talented team of Data Scientists and ML Engineers solving complex operational challenges in logistics.
You will own people leadership and culture, managing and coaching a high-performing team while fostering an inclusive, psychologically safe environment where engineers feel empowered to innovate. You will champion AI adoption across the organization, exploring how foundational models and agents can enhance internal workflows and products.
On the technical side, you will maintain deep connection to your team's work, diving into codebases during ramp-up and occasionally contributing to tooling initiatives. You will partner closely with a Staff ML Engineer who owns core technical architecture, ensuring seamless collaboration on deep technical challenges.
Your primary focus is delivery and execution: guiding the team in transitioning R&D prototypes into robust, scalable, production-ready ML systems while maintaining high engineering standards. You will work tightly with Product Managers to ruthlessly prioritize the backlog, focusing on initiatives with clear, measurable ROI that align technical possibilities with commercial goals. You will also collaborate extensively with the Data & AI Platform (MLOps) team to ensure scalable, maintainable solutions.
The role emphasizes outcome over output—you are fundamentally driven by ensuring your team's work translates into measurable business outcomes that drive margin and efficiency gains.
REQUIREMENTS:
- Leadership Experience: 3+ years successfully managing and scaling an Engineering, Machine Learning, or Data Science team
- Technical Foundation: 7+ years prior experience as Senior or Staff Individual Contributor with deep, hands-on experience deploying applied ML models into production
- Domain ML Experience: Direct, hands-on experience building, deploying, and maintaining recommender systems, pricing algorithms, or complex statistical models in production
- Technical Depth: High technical fluency with strong proficiency in Python, SQL, and Git. Deep familiarity with modern ML and data stacks (Snowflake, AWS, Datadog). Experience with Jupyter Notebooks, workflow orchestration (Airflow or Flyte), frameworks like TensorFlow or PyTorch, and LLM tooling (LangChain) required
- Academic Background: PhD in Mathematical, Statistical, Computer Science, or related quantitative field is a strong plus
- Management Craft: High emotional intelligence with strong track record mentoring senior talent, driving career growth, and leading teams through complex technical landscapes
- Business Acumen: Sharp sense for commercial value, ability to cut through noise, strong communication skills for managing expectations with executives and non-technical business leaders
