Data Scientist
Vor 5 Tagen
Data Scientist (Python / Azure ML) – Real-Time AI Decisioning - Frankfurt - €85–95K
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
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