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

If 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 exciting

• You’ll build production-grade ML systems that run continuously - not just notebooks
• Your work powers real-time decisioning across massive behavioural datasets
• True autonomy: low bureaucracy, high trust, modern engineering culture
• Close collaboration with applied AI, engineering, and product
• You get startup-like ownership backed by the stability of a well-established tech group
• This platform represents the future of how digital products decide and adapt at scale

What you’ll do

• Build end-to-end ML models for user profiling, recommendations, sorting, and incentives
• Use frameworks like scikit-learn, PyTorch, and GPU-accelerated toolkits
• Operate in Azure ML: data workflows, pipelines, training, environments, endpoints, compute
• Apply NLP and computer vision to enrich user and product understanding
• Validate and communicate results through A/B tests and performance monitoring

Who you are

A collaborative, ambitious data scientist who values quality, clarity, and measurable impact.

You bring:

• 5+ years hands-on machine learning in Python
• Practical cloud experience (Azure preferred)
• Strong ML/data toolkit knowledge:
• pandas, numpy, scikit-learn, PyTorch, dask, polars, rapids

You know or want to learn:

• Working with diverse datasets (Blob Storage, MongoDB, Elasticsearch, Cassandra)
• The full ML lifecycle: sampling, deployment, scaling endpoints (Kubernetes/AKS), monitoring
• Building scalable, production-ready ML pipelines - not just experiments

Apply 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