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