Data Scientist (Python / Azure ML) – Real-Time AI Decisioning - Frankfurt - €85–100K

Stellenbeschreibung:

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-\u201c\u201d decisioning platform that interprets behaviour at scale and takes hundreds of automated decisions every second . Their models transform raw behaviour signals into real-\u201c\u201d scores that drive personalised rankings, recommendations, and user experiences across millions of journeys.

You’ll be working with cutting-\u201c\u201d ML, real-\u201c\u201d 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)

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