Senior Operations Research Engineer (Applied AI, B2B SaaS)

The role of Senior Operations Research Engineer (Applied AI, B2B SaaS) is within a fast-growing B2B SaaS company. This company specializes in developing an AI-driven platform aimed at enhancing automotive aftersales operations. The platform uses digital twin technology and real-time optimization to improve workflows, efficiency, and decision-making for bodyshops, dealer groups, and multi-site operators. The main day-to-day responsibilities include designing and deploying AI models for prediction and decision-making using domain-specific operational data. This involves advancing scheduling and resource optimization, including multi-objective optimization, constraint handling, and stable re-planning. The role also entails building end-to-end models that learn from real operational data to improve planning accuracy over time. Additionally, the engineer will own the full AI lifecycle, from data analysis and feature engineering to model development, evaluation, and monitoring. They will also translate business problems into formal models and measurable outcomes. To qualify for this role, candidates must have a Master's or PhD in Computer Science, Mathematics, Physics, or a related field. Several years of experience in machine learning, particularly with tabular data and time series, are required. Strong knowledge of at least one deep learning framework, with a preference for PyTorch, is essential. Experience in designing optimization algorithms and heuristics for complex, constrained problems is also necessary. Proficiency in Python and the data science ecosystem, including tools like pandas and scikit-learn, is crucial. Additionally, experience with ML experiment tracking and model deployment using tools like MLflow, Docker, and Kubernetes is required. Fluency in English is a must. Nice-to-have skills include experience with LLMs or agentic systems, such as RAG, information extraction from unstructured data, and API-based workflows. The location, team, and conditions are not specified, but the company offers creative freedom in a young, technically ambitious team. The role provides direct impact on the product and customers, a modern tech stack, and a culture that encourages experimentation. The problem space is described as real-world and domain-rich, not a generic SaaS. What the role asks for: - Master's or PhD in Computer Science, Mathematics, Physics or a related field - Several years of experience in machine learning, particularly with tabular data and time series - Strong knowledge of at least one deep learning framework (PyTorch is preferred) - Experience designing optimisation algorithms and heuristics for complex, constrained problems - Proficient in Python and the data science ecosystem (e.g. pandas, scikit-learn) - Experience with ML experiment tracking and model deployment (e.g. MLflow, Docker, Kubernetes) - Fluency in English - Nice to have: Experience with LLMs or agentic systems (e.g. RAG, information extraction from unstructured data, API-based workflows)