Summary: As a Data Scientist, you will design and deploy machine learning models that power real-time personalization, owning ML projects end-to-end. Highlights: 1. Design and deploy ML models for ranking, recommendation, and personalization. 2. Own project workstreams from data preparation through production deployment. 3. Collaborate with Applied Scientists on new algorithm integration. **Location** Remote in Europe. **Albatross** At Albatross, we’re building the second pillar of AI: a perception layer that understands how users actually experience content, in real time. Trained on live user interactions, Albatross learns and reasons on the fly. Our technology powers real\-time, in\-session discovery by adapting to evolving user interests, in real\-time. We have raised significant funding and our platform already operates at scale, with billions of events being processed and hundreds of millions of predictions served. **The Role** As a Data Scientist, you will design and deploy machine learning models that power real\-time personalization for our customers. You will own defined workstreams of ML projects end\-to\-end, and you will work closely with Applied Scientists and Engineers to translate product and customer needs into scalable ML solutions. More specifically, you will: * Design and implement machine learning models for ranking, recommendation, and personalization. * Define feature engineering pipelines and modeling strategies for customer use cases. * Train, evaluate, and deploy models using our internal ML tooling and infrastructure. * Own project workstreams from data preparation through production deployment. * Collaborate with Applied Scientists to integrate new algorithms into production systems. * Contribute improvements to internal ML tooling and experimentation infrastructure. * Monitor model performance and iterate based on real\-world feedback. **Requirements** * Bachelor's degree in Machine Learning or STEM. * Strong background in machine learning, statistics, or data science. * Solid programming skills in Python. * Experience training and deploying ML models in production environments. * Familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX. * Experience working with large\-scale datasets and feature engineering pipelines. * Ability to work independently on moderately complex ML problems. * Strong communication skills in English. **Nice to Have** * Experience with recommender systems, ranking models, or search. * Experience with large\-scale experimentation and evaluation pipelines. * Familiarity with learning\-to\-rank models, bandits, or reinforcement learning. * Experience working with cloud environments such as AWS, GCP, or Azure. **Benefits** * Flexibility to work from anywhere across Europe. * Budget for learning and training, attend events and conferences.