Data Scientist (Python / Azure ML) – Real-Time AI Decisioning - Frankfurt - €75–85K

Data Scientist (Python / Azure ML) – Real-Time AI Decisioning - Frankfurt - €75–85KIf you want to build AI systems that make decisions in real time — not just produce offline predictions - this role is genuinely rare.This company has built a next-generation AI-decisioning platform that interprets behaviour at scale and takes hundreds of automated decisions every second. Their models transform raw behaviour signals into real-time scores that drive personalised rankings, recommendations, and user experiences across millions of journeys.You'll be working with cutting-edge ML, real-time inference, Azure ML pipelines, and a high-speed production environment where your work directly shapes the outcomes users see.Why this role is excitingYou’ll build production-grade ML systems that run continuously - not just notebooksYour work powers real-time decisioning across massive behavioural datasetsTrue autonomy: low bureaucracy, high trust, modern engineering cultureClose collaboration with applied AI, engineering, and productYou get startup-like ownership backed by the stability of a well-established tech groupThis platform represents the future of how digital products decide and adapt at scaleWhat you’ll doBuild end-to-end ML models for user profiling, recommendations, sorting, and incentivesUse frameworks like scikit-learn, PyTorch, and GPU-accelerated toolkitsOperate in Azure ML: data workflows, pipelines, training, environments, endpoints, computeApply NLP and computer vision to enrich user and product understandingValidate and communicate results through A/B tests and performance monitoringWho you areA collaborative, ambitious data scientist who values quality, clarity, and measurable impact.You bring:5+ years hands-on machine learning in PythonPractical cloud experience (Azure preferred)Strong ML/data toolkit knowledge:pandas, numpy, scikit-learn, PyTorch, dask, polars, rapidsYou know or want to learn:Working with diverse datasets (Blob Storage, MongoDB, Elasticsearch, Cassandra)The full ML lifecycle: sampling, deployment, scaling endpoints (Kubernetes/AKS), monitoringBuilding scalable, production-ready ML pipelines - not just experimentsApply now to avoid missing out!Keywords: Python, Azure ML, Machine Learning, Real-Time Decisioning, AI Decision Automation, scikit-learn, PyTorch, pandas, numpy, dask, polars, rapids, NLP, Computer Vision, A/B Testing, Behavioural Data, User Profiling, Ranking Models, Data Enrichment, Azure, Cloud ML Pipelines, Model Deployment, Kubernetes, AKS, Distributed Systems, MongoDB, Elasticsearch, Cassandra, Blob Storage, Data Streaming, Performance Monitoring, ML Observability, GPU Acceleration, Production ML, Travel Tech, Frankfurt