Senior Operations Research Engineer (Applied AI, B2B SaaS)

We are a rapidly expanding B2B SaaS firm delivering an AI‑driven platform that reshapes automotive after‑sales operations. The product equips bodyshops, dealer groups and multi‑site operators across Europe with tools to streamline workflows, boost efficiency and support data‑backed decisions. As a Senior Operations Research Engineer (Applied AI, B2B SaaS) you will join a dynamic startup‑like team that is already recognized for its award‑winning artificial intelligence, helping thousands of users in eight countries. Your daily work will revolve around creating and launching AI models that turn operational data into actionable predictions and decisions. You will extend scheduling and resource‑optimisation capabilities, handling multi‑objective goals, constraints and stable re‑planning. Building end‑to‑end pipelines that continuously learn from real‑world inputs, you will manage the full AI lifecycle, from data analysis and feature engineering to model evaluation, monitoring and deployment. Typical deliverables include capacity‑planning algorithms that respect skills, buffers and space limits, intelligent job‑to‑talent matching with dynamic weighting, and automated project creation from both structured and unstructured sources such as PDFs or free‑text descriptions. The role requires a strong academic background and hands‑on expertise. Candidates must hold a Master's or PhD in Computer Science, Mathematics, Physics or a closely related discipline, and have several years of experience applying machine‑learning techniques to tabular data and time‑series problems. Proficiency with at least one deep‑learning framework, preferably PyTorch, is essential, as is a proven track record designing optimisation algorithms and heuristics for complex, constrained scenarios. You should be fluent in Python and comfortable with the data‑science ecosystem (pandas, scikit‑learn), and have practical experience with ML experiment tracking and model deployment tools such as MLflow, Docker and Kubernetes. A self‑driven, solution‑oriented attitude that focuses on understanding the underlying business problem is also required, together with fluent English communication skills. While the core requirements are mandatory, additional experience with large language models or agentic systems (e.g., retrieval‑augmented generation, information extraction from unstructured data, API‑based workflows) is considered a plus. The position offers creative freedom within a technically ambitious, young team, direct impact on a real‑world product, and ownership of AI features from exploration to production. You will work on a domain‑rich problem space that goes beyond generic SaaS, using a modern tech stack that encourages experimentation and rapid iteration. What the role asks for: - Master's or PhD in Computer Science, Mathematics, Physics or related - Several years of machine learning experience with tabular data and time series - Strong knowledge of at least one deep learning framework, preferably PyTorch - Experience designing optimisation algorithms and heuristics for complex constrained problems - Proficient in Python and data‑science libraries such as pandas and scikit‑learn - Experience with ML experiment tracking and deployment tools like MLflow, Docker, Kubernetes - Self‑driven, solution‑oriented mindset focused on business problem understanding - Fluent in English - Nice to have: experience with LLMs or agentic systems