Senior Data Scientist Reinforcement Learning – Offer Intelligence (m/f/d)

We work at the intersection of pricing science, offer personalisation, advanced statistics, and scalable software. You'll build and bring into production state-of-the-art probabilistic models - like hierarchical Bayesian regression, SVI, or bandit algorithms - that power millions of pricing and offer decisions at SIXT every day. Beyond pricing, we're building the intelligence layer that decides which offer and which experience each customer sees, so your models shape both what we charge and what we show. From shaping the analytical question to deploying live models, you'll own the full lifecycle, supported by an innovation-friendly culture and great team. If you're excited by real-world impact, rigorous methods, and turning cutting-edge ideas into production - even if you don't tick every box - we'd love to hear from you!

• You architect and prototype advanced regression models (linear, GLM, mixed-effects, Gaussian Process) with a principled Bayesian approach, guiding pricing strategy at scale
• You design and productionise personalisation and recommendation systems - two-tower retrieval, embedding-based candidate generation, learning-to-rank, sequential/transformer models - with contextual bandits and off-policy evaluation to keep them learning from real interactions
• You apply techniques like SVI, MCMC, and importance sampling to enable robust decision-making under uncertainty, even with sparse, high-volume, or streaming data
• You build and maintain reproducible pipelines for feature engineering, label generation, and automated data validation using tools like Airflow or Dagster, ensuring high code quality and reliability
• You deploy models via FastAPI, Docker, or Kubeflow, and establish real-time monitoring with Bayesian control charts and performance dashboards to catch drift and anomalies early
• You design sophisticated A/B and multivariate tests, apply quasi-experimental and causal impact methods, and quantify the value of information to balance exploration and exploitation
• You collaborate with product, revenue, and customer experience leaders to translate ambiguous ideas into testable hypotheses, while mentoring peers and elevating Bayesian practice through internal publications and workshops